To reduce the total design and optimization time, numerical analysis with surrogate-based approaches is being used in turbomachinery optimization. In this work, multiple surrogates are coupled with an evolutionary genetic algorithm to find the Pareto optimal fronts (PoFs) of two centrifugal pumps with different specifications in order to enhance their performance. The two pumps were used a centrifugal pump commonly used in industry (Case I) and an electrical submersible pump used in the petroleum industry (Case II). The objectives are to enhance head and efficiency of the pumps at specific flow rates. Surrogates such as response surface approximation (RSA), Kriging (KRG), neural networks and weighted-average surrogates (WASs) were used to determine the PoFs. To obtain the objective functions' values and to understand the flow physics, Reynolds-averaged Navier-Stokes equations were solved. It is found that the WAS performs better for both the objectives than any other individual surrogate. The best individual surrogates or the best predicted error sum of squares (PRESS) surrogate (BPS) obtained from cross-validation (CV) error estimations produced better PoFs but was still unable to compete with the WAS. The high CV error-producing surrogate produced the worst PoFs. The performance improvement in this study is due to the change in flow pattern in the passage of the impeller of the pumps.

Low fidelity model assisted design optimization of turbomachines has reduced the total computational and experimental costs. These models are called surrogate models which mimic the actual experiments or simulations. The surrogate models can generate thousands of approximate results from a few samples, making it easy to locate the optimal solution. Ample articles reported surrogate assisted design optimization of centrifugal pumps. In this article, the authors try to give a brief overview of the surrogate based optimization technique along with its historical applications and trend of the recent use. The various key design parameters which affect the performance of the centrifugal pump have also been discussed. The effectiveness of the surrogate based optimization technique and corresponding performance metrics have been discussed.

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Organic fiber-based biocomposites have gained prominence in a variety of sectors over the last four to five years due to their exceptional mechanical and physical properties. Natural fiber-based composites are increasingly being employed in autos, ships, airplanes, and infrastructure projects. The current study will look at the effect of nanotitanium oxide (TiO2) fillers on the properties of hybridised jute-hemp-based composites. In this work, TiO2-filled biocomposites were created using the hand layup method in hybrid jute-hemp composites containing jute fiber mats, woven hemp mats, and epoxy resin. After nanotitanium oxide fillers were injected in various weight proportions, the mechanical properties of fiber-reinforced polymers were investigated. The mechanical properties of laminated composites were tested using the ASTM standard. Compared to 2 and 4 wt.% of TiO2, the 6 wt.% was provided the highest mechanical strength. Among the different types of specimen, the E-type specimen (30 wt.% of hemp, 7 wt.% of jute, 57 wt.% of epoxy, and 6 wt.% of TiO2) gives their highest contribution, i.e., for tensile 24.21%, for flexural 25.03%, and for impact 24.56%. The scanning electron microscope was utilized to analyse the microstructures of nanocomposites.

Pumping viscous fluids using centrifugal pumps in the subsea industry is very common. The pump performance degrades drastically when the viscosity of fluids increases, which ultimately gives rise to the installation and oil production cost. Their design optimization can lead to a significant improvement in their performance. Therefore, this study presented the effect of impeller geometry on pumping fluid viscosity through impeller design optimization. Here, pump operation is simulated numerically by solving the Reynolds-averaged Navier–Stokes (RANS) equations at different flowrates. Experimental testing is also performed using the same oils, for numerical validation. Artificial neural-network-assisted multiobjective optimization was performed with two independent design parameters; wrap angle and splitter blade length of impeller, with head and input power as objective functions. Wrap angle and splitter blade length, both significantly affect pump performance while pumping viscous oils; as the oil viscosity increases, increasing splitter length and decreasing wrap angle improve the head significantly.