Computational Materials Capabilities · Macro-Mechanical Polycrystal Single Crystal Molecular...
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Computational Materials CapabilitiesJoel D. Kress
Theoretical Division, Los Alamos National Laboratory
Macro-Mechanical
Polycrystal
Single Crystal
MolecularDynamics
Quantum Mechanics
Experiment
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Strain
Time / Length Scales
We are staffed to meet complex materials problems with interdisciplinary approaches:taking theory to models to numerical implementation to production codes
Irradiation creep and growth
à nucleation and evolution of dislocation
loops by climb
Thermal creepà thermally
activated obstacle overcoming by
dislocations
Radiation hardening à Dislocations
interacting with loops, precipitates & other
dislocations
VPSC CodePolycrystal model of creep accounting for crystallo-graphic
mechanisms, texture, processing conditions
FE-VPSC: ABAQUS or MOOSE/BISONCoupled FE-VPSC code gives dimensional changes & strength of grid
assembly under complex conditions of dose, stress & temperature
VPSC accounts for grain orientation and interaction
with neighbors
e.g. creep and growth of P91,
Zr4 etc.
FE-VPSC allows, e.g. prediction of gap
opening in fuel rod assemblies
Molecular Dynamics and Discrete Dislocation Dynamics Interaction between dislocations and
irradiation induced defects
Formation of this network comprising of mixed dislocations could be explained witha two-step process which is quite analogous to the formation of screw dislocation net-work in Zircaloy-4 as described in detail elsewhere [40]: Firstly, an interaction betweentwo <a> type mixed dislocations gliding on their respective prism planes produces thethird type of <a> type dislocation according to the following relation (Figure 16):
a32 !1 !1 0½ " þ a
3!1 2!1 0½ " ¼ a
3½1 1 !2 0" (7)
Figure 12. Sub-boundary consisting of edge dislocations in the specimens crept at (a) 500 °Cand 64.3 MPa and (b) 500 °C and 78.5 MPa in regime II. The ½1 !1 0 0" diffraction pattern showsthe orientation of the grain. The dashed line depicts the cut section of the plane on which thedislocation line vector lies, which is perpendicular to the <a> direction in the left image.
Figure 13. (a) A TEM micrograph depicting dislocations forming hexagonal network on theplane normal to ½1 1 !2 3" crystallographic axis in the specimen deformed at 500 °C and64.3 MPa. The inset shows the diffraction pattern of ½1 1 !2 3". (b) A TEM micrograph at a highermagnification showing the dislocation network.
1670 B. Kombaiah and K. Linga Murty
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Multiscale materials modeling
Multiscale modeling paradigm
Additive manufacturing process aware modeling and simulation
• iteratively improve predictions # 20140013DR model predictions
Physics modelsDomain knowledge
1 2
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Statistical inference
experimentaldesign
DataAdaptive
with uncertainties
Materialssynthesis and
characterization
New firstprinciples
calculations
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Success balanc
e trade
-offs
MATERIALS PROJECT, etc
y(x)=f(x)±e(x)
Approach:‘Exploit vs Explore’ high
dimensional search space of possible candidates via global optimization
Accelerated materials discovery via adaptive design
Ferritic Creep-Resistant Steels
Goal: Optimal learning of materials with targeted properties by guided experiments and calculations
• Phase field approach to microstructure solidification simulation
• Ferrite component of steel observed to solidify with dendritic structures
• Finite element methods provide high-order spatial discretization on unstructured 2D and 3D meshes
• Implicit time integration allows for stable, second-order with large time steps
• Unstructured mesh allows for irregular (curved) geometries
Multiple dendrite growth along flat boundary
3D single dendrite growth
Microstructure capability development
Multiple dendrite growth along curbed boundary, unstructured mesh
Metallic based multi-layered nano-composites are recognized for their increased plastic flow strength and indentation hardness, increased ductility, improved radiation damage resistance, improved electrical and magnetic properties, and enhanced fatigue failure resistance compared to conventional metallic materials.
Tom Nizolek, UCSB summer PhD student
Cu/Nb nano-layered compositesby severe plastic deformation
MicrostructureModeling
PerformanceModeling
WeldPool
Direct numerical simulation of grain growth
Thermal - mechanical models to predict elastic/plastic/damage and failure processes
ProcessModeling
PropertiesModeling
Initial grain distribution (Nucleation site)
Final grain shape and composition
Polycrystal models to determine elastic/plastic damage properties
Liquid/solid phase change Solid/solid phase transformation
AM specific interface properties
Moving heat source Polycrystal and grain boundary properties
TRUCHAS code3D multi-physics microstructure-aware solidification capability
• Eight geometric realizations – 84 Cu grains, 79 Nb grains• Five crystallographic realizations for each – 420 Cu grains, 395 Nb grains• Multi-point constraint linking top surface to both sides to preserve area• Temperature constant at 298K• No degrees of freedom at the bi-material interface
Cu
Nb
9grains
12grains
Cu
Nb
12grains
9grains
8grains
8grains
13grains
8grains
Cu/Nb plane strain compression simulations
DataManagementFramework
Process Models
SolidIn
SolidOut
GasIn
GasOut
UtilityIn
UtilityOut
Basic Data Submodels
Optimized Process
GHX-001CPR-001
ADS-001
RGN-001
SHX-001
SHX-002
CPR-002
CPP-002ELE-002
ELE-001
FlueGasCleanGas
RichSorbent
LP/IPSteamHXFluid
Legend
RichCO2Gas
LeanSorbent
ParallelADSUnits
GHX-002
InjectedSteam
CoolingWater
CPT-001
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5 3
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S1
S2
S3
S4
S5
S6
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CYC-001
AlgebraicSurrogateModels
Uncertainty
Uncertainty
Uncertainty
Uncertainty
SuperstructureOptimization
Uncertainty
Simulation-BasedOptimization
Uncertainty
ProcessDynamicsand
Control
CFDDeviceModels
UncertaintyQuantification
2016 Award
Carbon Capture Simulation Initiative
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Benefits of Bayesian approach• Uses data already available and
thought to be relevant
• Check on consistency of information from different sources
• Allows inclusion of external information
• Appropriately propagates uncertainty at all levels
Key elements
• Analysis combines subject matter expertise with statistical methods
• Careful assessment of model assumptions and comparison to reality
• Leveraging data from multiple sources
Flight Data
Testset Data
MaintenanceData
Accelerated Testing Data
Prediction of future reliability (system & component)
Identification of critical parts
Matching of different data sources
Age
Reliability
Component Reliabilities
System Reliability
Reliability (change with age) using multiple data sources
LA-UR-17-29050