Additive manufacturing (AM) of metals can be broadly accomplished via two defined technologies: powder bed fusion and directed energy deposition. During AM fabrication, the melted feedstock material experiences fast thermal cycling due to the layer-by-layer deposition process resulting in microstructures and properties that are drastically different from the traditionally manufactured parts. For AM to become a viable process for fabricating critical components made of high-performance structural alloys, such as AlSi10Mg, a comprehensive understanding is required toward developing the process-structure-property relationships prevalent in AM. AlSi10Mg, with its good castability, strength, hardness, and dynamic properties, is typically used to fabricate structural components that are required to withstand high loads. This alloy has been consolidated predominantly by the laser powder bed fusion (L-PBF) method and several critical mechanical properties, such as fatigue, have been reported to date. This article, first, summarizes the as-deposited and heat-treated microstructures of AlSi10Mg specimens fabricated by L-PBF. Then, the article discusses the linkages among the feedstock properties, printing parameters, specimen geometry, post-processing techniques, and fatigue properties. This discussion is followed by a section on the fatigue life prediction of AlSi10Mg specimens using computational modeling. Finally, the article identifies critical research gaps and pinpoints future research opportunities.

The education sector has suffered a catastrophic setback due to the ongoing COVID pandemic, with classrooms being closed indefinitely. The current study aims to solve the existing dilemma by examining COVID transmission inside a classroom and providing long-term sustainable solutions. In this work, a standard 5 × 3 × 5 m3 classroom is considered where 24 students are seated, accompanied by a teacher. A computational fluid dynamics simulation based on OpenFOAM is performed using a Eulerian–Lagrangian framework. Based on the stochastic dose–response framework, we have evaluated the infection risk in the classroom for two distinct cases: (i) certain students are infected and (ii) the teacher is infected. If the teacher is infected, the probability of infection could reach 100% for certain students. When certain students are infected, the maximum infection risk for a susceptible person reaches 30%. The commonly used cloth mask proves to be ineffective in providing protection against infection transmission, reducing the maximum infection probability by approximately 26% only. Another commonly used solution in the form of shields installed on desks has also failed to provide adequate protection against infection, reducing the infection risk only by 50%. Furthermore, the shields serve as a source of fomite mode of infection. Screens suspended from the ceiling, which entrap droplets, have been proposed as a novel solution that reduces the infection risk by 90% and 95% compared to the no screen scenario besides being completely devoid of fomite infection mode. The manifestation of infection risk in the domain was investigated, and it was found out that in the case of screens the maximum infection risk reached the value of only 0.2 (20% infection probability) in 1325 s.

In chemical industries, multiphase flows in a bubble column reactor are frequently observed. The nonlinearity associated with bubble hydrodynamics, such as bubble–bubble and bubble–liquid interactions, gives rise to complex spatiotemporal patterns with increased gas or liquid velocities, which are extremely difficult to model and predict. In the current study, we propose a new, computationally efficient recurrence-based approach involving the angular separation between suitably defined state vectors and implement it on the experimental multiphase flow variables. The experimental dataset that consists of image frames obtained using a high-speed imaging system is generated by varying air and water flow rates in a bubble column reactor setup. The recurrence plots using the new approach are compared with those derived from conventional recurrence, considering standard benchmark problems. Further, using the recurrence plots and recurrence quantification from the new recurrence methodology, we discover a transition from a high recurrence state to a complex regime with very low recurrence for an increase in airflow rate. Determinism exhibits a rise for the decrease in airflow rate. A sharp decline in determinism and laminarity, signifying the quick shift to complex dynamics, is more prominent for spatial recurrence than temporal recurrence, indicating that the rise in airflow rate significantly impacts the spatial location of bubbles. We identify three regimes that appeared as distinct clusters in the determinism–laminarity plane. The bubbly regime, characterized by high values of determinism and laminarity, is separated by an intermediate regime from the slug flow regime, which has low determinism and laminarity.