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~0.5 m Computational grid size Process ~5 mm REV Modeling Microsegregation In Metal Alloys – Vaughan Voller, University of Minnesota 1 of 14 Can we build a direct-simulation of a Casting Process that resolves to all scales? Difficult due to wide range of Time and Space Scales ~ 50 m solid representative ½ arm space sub-grid model g Scales in a “simple” solidification process model
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chi ll A Casting The REV Nucleation Sites columnar equi-axed The Grain Envelope The Secondary Arm Space The Tip Radius The Diffusive Interface ~ 0.1 m ~10 mm ~ mm ~100 m ~10 m ~1 nm 10 3 10 1 10 -1 10 -3 10 -5 10 -7 10 -9 10 -9 10 10 -3 10 -1 Length Scale (m) interface kinetics nucleation solute diffusion growth grain formation casting heat and mass tran. Time Scale (s) Scales in Solidification Processes To handle with current computational Technology require a “Micro-Macro” Model See Rappaz and co-workers Example a heat and Mass Transfer model Coupled with a Microsegregation Model 2 of 14 (after Dantzig)
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~0.5 m ~ 50 m solid ~5 mm Computational grid size ProcessREV representative ½ arm space sub-grid model g from computation Of these values need to extract Solidification Modeling -- 3 of 14 Micro segregation—segregation and solute diffusion in arm space
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A C Primary Solidification Solver Transient mass balance equilibrium g ClCl T Iterative loop g model of micro-segregation (will need under-relaxation) 4 of 14 Give Liquid Concentrations
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liquid concentration due to macro-segregation alone Micro-segregation Model In a small time step new solid forms with lever rule on concentration Q -– back-diffusion Need an easy to use approximation For back-diffusion transient mass balance gives liquid concentration Solute mass density before solidification Solute mass density of new solid (lever) Solute mass density after solidification 5 of 14 Solute Fourier No. ½ Arm space of length takes t f seconds to solidify
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The parameter Model --- Clyne and Kurz, Ohnaka 6 of 14 For special case Of Parabolic Solid Growth And ad-hoc fit sets the factor and In Most other cases The Ohnaka approximation Works very well
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The Profile Model Wang and Beckermann Need to lag calculation one time step and ensure Q >0 7 of 14 m is sometimes take as a constant ~ 2 BUT In the time step model a variable value can be use Due to steeper profile at low liquid fraction ----- Propose
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Coarsening Arm-space will increase in dimension with time This will dilute the concentration in the liquid fraction—can model be enhancing the back diffusion A model by Voller and Beckermann suggests If we assume that solid growth is close to parabolic In profile model m =2.33 in Parameter model 8 of 14
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Remaining Liquid when C =5 is Eutectic Fraction 9 of 14 Constant Cooling of Binary-Eutectic Alloy With Initial Concentration C 0 = 1 and Eutectic Concentration C eut = 5, No Macro segregation, = 0.1 Use 200 time steps and equally increment 1 < C l < 5 Calculating the transient value of g from Parameter or Profile
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Results are good across a range of conditions Note Wide variation In Eutectic 10 of 14
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Parabolic solid growth – No Second Phase – No Coarsening Use 10,000 equal of g C 0 = 1, = 0.13, = 0.4 Use To calculate segregation ratio 11 of 14
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Performance of Models under parabolic growth no second phase Prediction of segregation ratio in last liquid to solidify (fit exponential through last two time points) 12 of 14
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Parameter Robust Easy to Use Poor Performance at very low liquid fraction— can be corrected Profile A little more difficult to use With this Ad-hoc correction Excellent performance at all ranges Two Models For Back Diffusion A C Predict g predict C l predict T Calculate Transient solute balance in arm space Solidification Solver 13 of 14 Account for coarsening My Method of Choice
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1000 2 0.6667 Year “Moore’s Law” current for REV of 5mm Voller and Porte-Agel, JCP 179, 698-703 (2002).5m I Have a BIG Computer Why DO I need an REV and a sub grid model ~ 50 m solid ~5mm (about 10 6 nodes) 14 of 14 2055 for tip Model Directly (about 10 18 nodes) Tip-interface scale
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