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Challenge 3 of the 2022 NIST additive manufacturing benchmark (AM Bench) experiments asked modelers to submit predictions for solid cooling rate, liquid cooling rate, time above melt, and melt pool geometry for single and multiple track laser powder bed fusion process using moving lasers. An in-house developed Additive Manufacturing Computational Fluid Dynamics code (AM-CFD) combined with a cylindrical heat source is implemented to accurately predict these experiments. Heuristic heat source calibration is proposed relating volumetric energy density (ψ) based on experiments available in the literature. The parameters of the heat source of the computational model are initially calibrated based on a Higher Order Proper Generalized Decomposition- (HOPGD) based surrogate model. The prediction using the calibrated heat source agrees quantitatively with NIST measurements for different process conditions (laser spot diameter, laser power, and scan speed). A scaling law based on keyhole formation is also utilized in calibrating the parameters of the cylindrical heat source and predicting the challenge experiments. In addition, an improvement on the heat source model is proposed to relate the Volumetric Energy Density (VEDσ) to the melt pool aspect ratio. The model shows further improvement in the prediction of the experimental measurements for the melt pool, including cases at higher VEDσ. Overall, it is concluded that the appropriate selection of laser heat source parameterization scheme along with the heat source model is crucial in the accurate prediction of melt pool geometry and thermal measurements while bypassing the expensive computational simulations that consider increased physics equations. [ABSTRACT FROM AUTHOR]

Freestream turbulence in rivers is a key contributor to the flux of dissolved nutrients, carbon, and other ecologically important solutes into porewater. To advance understanding of turbulent hyporheic exchange and porewater transport, we investigate flow over and through a rough bed of spheres using large eddy simulation (LES). We apply double averaging (combined space and time averaging) to the LES results to determine the mean velocity distribution, momentum balance, and drag forces. Our simulations show large‐scale freestream structures interacting strongly with vortices generated at the surfaces of individual spheres to control turbulent momentum fluxes into the bed. The transition between turbulent flow and Darcy flow occurs over the first row of spheres, where turbulence decays rapidly and turbulent kinetic energy, Reynolds stress, and drag forces peak. Below this region, turbulence is only present in the high‐velocity flow in open pore throats. Experimental observations suggest that minimum mean porewater velocity occurs in the first open pore space below the transition region, but our results show that the minimum occurs between the first and second pore spaces. The simulation mean porewater velocities are approximately half those captured in measurements because the model resolves the entire flow continuum while measurements can access high‐velocity fluid in open pores. The high‐resolution dual time‐space averaging of the LES resolves both turbulent and mean flow features that are important to interfacial solute and particle fluxes, providing a means to include turbulent hyporheic exchange in upscaled river models, which has not been achieved to date. Key Points: We use LES to identify the transition region between turbulent and hyporheic porewater flowWe extend double averaging methods for LES to better represent drag forceLES double averaging more completely represents turbulent hyporheic exchange and informs upscaled river models [ABSTRACT FROM AUTHOR]

In this article, the author refers to a study related to blood flow analysis of the aortic arch using computational fluid dynamics (CFD). It mentions that the researchers use patient-specific computer models of the thoracic aorta in combination with CFD. It states that echo cardiography and respiration-controlled 4D flow magnetic resonance imaging (MRI) enables assessment of cardiovascular haemodynamics in great vessels and the heart.