Journal: Scientific Reports

Article Title: 3D-Spatial encoding with permanent magnets for ultra-low field magnetic resonance imaging

doi: 10.1038/s41598-018-37953-1

Figure Lengend Snippet: PMA design for the ULF-MRI developed at the Centre for Advanced Imaging (CAI). It comprises a switchable Array A with 12 magnets for sample pre-polarization field B p , Arrays B and C with 24 magnets and 36 magnets, respectively, and Array D shown with one encoding magnet. With the chosen design parameters, described in the methods section, B p ≈ 48 mT parallel to the x-axis and B m ≈ 140 µT aligned with the y-axis. The magnetisation directions are indicated by green (Array A), blue (Array B) and red (Array C) arrows for each magnet. The insets show the simulated fields as surface plots (COMSOL colour scheme Rainbow) on the cubic FOV, located at the centre of the arrays, with a section removed to view the fields within the FOV. ( a ) Array A with the Halbach magnetization pattern. The B p distribution illustrated in the inset has the typical field characteristics of a cylindrical dipole Halbach array. ( b ) Array A with the tangential magnetisation pattern ( B p = off), with B m shown in the inset. ( c ) Detail of FOV with the 3D cross-shaped sample used for this study. For illustration purposes the front section of the sample has been removed. Shown are two small encoding magnets Ma1 and Ma2 with position parameters used in Equations and . The inset shows the magnetic field B e generated by Ma1 with magnetisation m at an arbitrary location. The red arrows indicate the magnetic field orientation at discrete locations within the FOV; their length indicates the local field strength.

Article Snippet: Instead, full scaled 3D computational models were created in COMSOL © , a commercial finite element method (FEM) based simulation platform (version 5.0, modules AC/DC and Magneto-static, COMSOL Inc., Burlington, MA 01803, USA) was employed for numerical analysis to evaluate the static and dynamic magnetic fields.

Techniques: Imaging, Generated

Journal: Journal of manufacturing science and engineering

Article Title: A Review of Model Inaccuracy and Parameter Uncertainty in Laser Powder Bed Fusion Models and Simulations

doi: 10.1115/1.4042789

Figure Lengend Snippet: Inputs, outputs, and characteristics of melt pool models

Article Snippet: Thermal conductivity of material Density of material Specific heat capacity Melting temperature Latent heat of fusion Laser power Scan speed Preheat temperature Powder bed absorptivity Laser power Beam size Layer thickness Scan speed Powder bed absorptivity Packing density/porosity Powder bed thermal conductivity Latent heat of fusion Density of material Emissivity Convection coefficient Preheat temperature Solidus and liquidus temperatures Beam power Scan speed Beam size Layer thickness Density of material at liquid and solid states Viscosity Surface tension coefficient Gravity Thermal conductivity of liquid and solid Specific heat capacity of liquid and solid Solidus and liquidus temperatures Boiling temperature Preheat temperature Latent heat of fusion and vaporization Emissivity Saturated vapor pressure Powder packing density Powder bed absorptivity Temperature coefficient of surface tension Powder size distribution , Rosenthal’s based thermal model [ 45 , 87 ]FEM-based thermal model [ 50 , 57 , 74 – 76 , 88 – 92 ]Lattice Boltzmann method (LBM) [ 13 , 56 , 93 ]Computational fluid dynamics (CFD) [ 63 – 65 , 79 – 83 , 94 – 97 ] , Uses isotherm migration method Tracks the location of solidification front Uses temperature-dependent material properties Assumes point heat source Assumes surface energy distribution Assumes powder bed as continuum body Uses FEM software: ANSYS, ABAQUS, 3DSIM, etc. to solve heat conduction governing equations and boundary conditions Volumetric energy distribution can be considered Assumes continuum powder bed Ignores the fluid dynamics of the melt pool and associated effects Simulate the fluid dynamics of melt pool by solving discretized Boltzmann equation on a lattice Considers individual powder particles Considers effects of capillary, wetting, Marangoni, recoil pressure, vaporization Numerically solves mass, momentum, and energy conservation equations using CFD methodologies (FEM, FDM, and/or FVM) coupling with VOF Utilizes CFD tools like OpenFOAM and Flow-3D Considers powder bed as distributed particles Considers effects of capillary, wetting, Marangoni, recoil pressure, and vaporization , Melt pool width Melt pool length Temperature field Melt pool width Melt pool length Melt pool depth Temperature field Melt pool width Melt pool length Melt pool depth Surface roughness Porosity: gas pores, intertrack voids Layer connection defects Balling effect and nonuniformity Keyhole defects.

Techniques: Convection, Viscosity, Migration, Software