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In this study, an Unsteady Reynolds-Averaged Navier-Stokes (URANS) model is demonstrated its suitability for studying the flow and performance of open marine propellers and waterjet pumps. First, the accuracy of the URANS model is validated by studying turbulent flow past counter-rotating propellers (CRPs). Specifically, experimental data from Miller (1976) is employed for comparison against the URANS results. Subsequently, URANS is used to study the flow and performance of an Office of Naval Research (ONR) axial flow waterjet pump (AxWJ-2). Due to the large number of degrees of freedom for both simulations, parallel computations over 80 cores are performed. For the CRP study, torque and thrust coefficients are assessed against a range of advance ratios, ensuring a Reynolds number of less than 600,000. For the waterjet, torque and head coefficients are computed for a range of flow rates at a Reynolds number of 1.25 million. For both studies, two levels of mesh resolution are utilized. The finer meshes of both studies contained roughly four times the total number of cells employed in their respective coarser counterparts. These refinements lead to minor improvements, suggesting good grid resolutions with the coarser grids. Across all advance ratios for the CRP set, the URANS torque and thrust coefficients show good agreement with experimental results, remaining within 10% difference. The torque and head coefficients for the waterjet displayed even better agreement, with the greatest error across all flow conditions remaining under 3%. Moreover, URANS studies revealed that the stator is responsible for 20% of the waterjet’s power production. 

The growing reliance on online services underscores the crucial role of recommendation systems, especially on social media platforms seeking increased user engagement. This study investigates how recommendation systems influence the impact of personal behavioral traits on social network dynamics. It explores the interplay between homophily, users’ openness to novel ideas, and recommendation-driven exposure to new opinions. Additionally, the research examines the impact of recommendation systems on the diversity of newly generated ideas, shedding light on the challenges and opportunities in designing effective systems that balance the exploration of new ideas with the risk of reinforcing biases or filtering valuable, unconventional concepts.