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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]

Creation of new alloys through in-situ alloying in additive manufacturing is being actively explored to improve the properties and performance of manufactured parts. Multiphysics models are often used to investigate the underlying physics controlling AM processes using high-fidelity simulations of individual powder particle melting within the meltpool. In this paper, we present a novel multiphysics-multiphase model for simulating multicomponent powder beds during the AM process. A convection–diffusion formulation for component mass fraction, in conjunction with the Navier–Stokes equations and enthalpy form of the energy equation, enable us to predict the phase evolution in multicomponent beds and the dispersion of solute metals in the melt pool during the process. A non-equilibrium phase diagram (NEPD) model that incorporates the associated phase diagram is introduced to obtain the mass fraction in both solid and liquid phases as well as local temperature and liquid fraction by efficiently solving a set of nonlinear equations locally at each computational cell. This work introduces a numerical method for multicomponent Al-Zr binary material system for light-weight aluminum alloys, specifically demonstrating its efficacy and accuracy for eutectic solidification of this multicomponent. [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.

Selective Electron Beam Melting (SEBM) is a promising powder-based metallic Additive Manufacturing (AM) technology. However, most powder-scale modeling efforts are limited to single track process, while it is also difficult to experimentally observe the interaction between tracks and layers. In this study, we develop an integrated modeling framework to investigate the SEBM process of multiple tracks and multiple layers. This approach consists of a Discrete Element model of powder spreading and a Computational Fluid Dynamics (CFD) model of powder melting. These two models exchange 3D geometrical data as a cycle to reproduce the manufacturing process of multiple tracks along various scan paths in multiple powder layers. This integrated modeling approach enables further understanding of how current tracks and layers interact with previous ones leading to inter-track/layer voids. It also incorporates more influential factors, particularly the layer-wise scan strategy. The inter-layer/track voids due to the lack of fusion are systematically discussed in light of our simulation results which qualitatively agree with experimental observations in literature. [ABSTRACT FROM AUTHOR]