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

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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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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

1

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