# Presentation: 524096

## The slides that follow were presented at the PSAAP Bidder's Meeting May 16-17, 2006 and represent the ASC Trilab authors and interests as presented in the associated White Paper for this subject area.

- Predictive Science Academic Alliance Program (PSAAP)

## Materials Stability and AgingNNSA ASC Predictive Science Academic Alliance Bidders ConferenceMay 16th, 2006

**Justine Johannes, SNL, **_**[jejohan@sandia.gov](mailto:jejohan@sandia.gov)**_
**Christian Mailhiot, LLNL, **_**[mailhiot1@llnl.gov](mailto:mailhiot1@llnl.gov)**_** **
**Luis Morales, LANL, **_**[lmorales@lanl.gov](mailto:lmorales@lanl.gov)**_
**Acknowledgements: Jeff Braithwaite, Dick Salzbrenner, Roger Clough, Eliot Fang, etc. **

## Predicting Materials Stability and Aging To Avoid Surprise

**The Labs have a mission that requires predictive models of materials aging**
**Our systems have a extended lifetime,**
**     ****over 30 years**
**Inspection is non-trivial**
**Consequence of failure are **
**     ****unacceptable.**
**	**
**	**** **

**There are other industries/missions that have similar constraints – these activities can benefit from fundamental understanding and the resulting predictive capability.**** **

## Severe cracks found at one nuclear power reactor

**Infrastructure**
**Utilities**
**Nuclear power plants**
**Electrical facilities**
**NASA**
**The Military**
**Ships**
**Planes**
**Etc....**

**Severe cracks found at one nuclear power reactor**

**What Other Industries Would Benefit From Materials Aging Models?**

## How Do We Think About Predicting the Life Cycle of a “System” 

**Challenges in predicting normal failure rates and the “end of life”:**
**Accumulation of aging on the overall system is challenge**
**Meaningful accelerated aging can be difficult for long “service life” parts and for varying environments.**
**Critical physics can change during lifetime. **
**Harsh environments present unique challenges.**
**Embedded components that can’t be easily monitored create challenge.**

## Aging & Reliability

**Material aging is a **_**natural process**_** in a given environment.**
**Change of environment could result in change of aging process. **

## What is needed for predictive aging models?

**Understand mechanisms of materials aging and failure**
**Establish basic knowledge of how materials age that is useful for addressing reliability and materials performance as a function of age and environment **
**Provide physical basis necessary for an effective long-term predictive capability**
**Identify how manufacturing processes can introduce latent defects or new materials that may be manifested as an aging concern in the long term**
**Predict materials changes**
**Quantify the effect of material property changes on the desired performance – can result in large computational requirements**
**Develop understanding of what and how to monitor effects**
**Challenges exist in developing the basic phenomena and validating the models. **

## Setting Priorities for Materials Aging Links the Likelihood and Consequence

- electric contact degradation

- corrosion of glass

- SCC of bolts, lugs
- in handling gear

- corrosion of Al covers/ housings

- degradation of o-rings & electrical insulation

- interface & adhesive fracture/delamination

- oxidation, etc. of coatings, paints, etc.

- fracture/SCC of screws & connectors

- fracture/SCC of ceramics and glasses

- diffusive degradation of electronic joints

- stress voiding

- yielding of springs/actuators

- fracture of pressure vessels

- corrosion in
- electronics

- increase in friction coefficient

- instability of energetic materials

- TMF of solder joints

- cracking of organic composite housings

- electrical failure of solid encapsulants

**changes in internal**
**weapon atmosphere**

## Materials of Interest Are Broad....A Few Examples 

**Solder **
**Corrosion of electrical devices**
**Welds**
**Lubricants **
**Electromechanical contacts **
**Thin film metal/metal interfaces**

**Materials of Interest Are Broad....****A Few Examples**** **

**Organics**
**   ****O-rings**
**   ****Foams and encapsulants **
**   ****Silicones**
**   ****GMB epoxies**
**   ****Adhesive interfaces **
**   ****Energetic materials **
**Inorganics**
**   ****Ceramics and glasses**
**   ****Dessicants**

**Fundamental understanding of aging mechanisms and their effects on the materials performance can be challenging:**
**Length and time scale challenges**
**Multi-physics needs**
**Validation challenges**

## Predicting solder degradation & electronics lifetime is a cross-discipline, multi-scale challenge

- Circuit board design?
- Loading?
- Material behaviors?
- Operation / storage environment?

**One example where we have worked extensively to develop predictive models**

## Solder degradation and interconnect failureis a major concern in our business

## A multiscale modeling approachwas developed to address TMF in solder joints

**A multiscale modeling approach****was developed to address TMF in solder joints**

- PI: Liz Holm (SNL/NM)

**Notes:**
Approach and Accomplishments
This project takes an RDA approach to the problem of thermomechanical fatigue modeling. Microscale research provides the physical foundation for larger length scale models by elucidating microstructural failure mechanisms (upper left).  In this effort, a JAS3D FEM-based polycrystal plasticity model is iteratively coupled to a Front-Tracking grain growth model to simulate plastic damage accumulation in a strained polycrystal. Damage accumulates near grain triple junctions, as shown in the upper right figure.
Mesoscale development focuses on methods for deriving continuum/ constitutive behavior from the details of microstructure (center left).  This year, our Material Point Method code was applied to tin polycrystals, deformed in shear.  Stress networks are observed, as shown in the middle right figure.
Macroscale FEM methods are our applications of choice for component modeling (bottom left). A viscoplastic continuum damage (VCD) model was developed to include microstructural effects in a constitutive model suitable for incorporation in FEM continuum calculations.  Parameters within the model evolve as microstructure changes; in turn, the mechanical response is altered.The bottom right figure shows an example TMF simulation of a chip capacitor surface mount solder joint, where the color scale indicates the level of microstructural damage according to the VCD model.  High damage is occurs near the chip surface, consistent with experimental observations.

## Multiscale materials modeling & simulationCan play a critical role in predicting materials aging

***... ******there is an increasing need as we seek to understand and control the behavior of increasingly complex materials.***

## Material Aging Interest Includes Corrosion

**Type I – corrosion product layer growth: devices or ****components that contain copper and/or silver AND **
**a sulfide or ozone-containing gaseous contaminant ****is present, but**
**importantly, moisture does NOT have to be present**
**gas signatures are typically not present due to low ****concentrations and chemical sinks**
**Type II – corrosion voiding: aluminum in microelectronic devices ****(including hermetic packaged) and nichrome in resistors AND**
**an aggressive contaminant (e.g., halide) and moisture are both present**
***Corrosion is difficult to predict because of active/passive nature of engineering alloys, the presence of latent manufacturing defects in product, and the knowledge of the environment (even internal) does not guarantee that we know the local environment in which corrosion occurs.***
***Predictive Model Challenges: ***
***	******Complex Chemistry, Difficult to validate, Stochastic Nature, ***
***	******Multiphysics, Bridging Length Scales, etc.***

## Modeling and Simulation of Corrosion in Microelectronics

**Atomistic and quantum mechanical calculations and experimental discovery are used to study the underlying microscopic mechanisms.**
**Corrosion product morphology**
**Liquid-state chemistry and transport**
**Solid-state diffusion**
**The continuum level models will be used to provide continuum treatment of multi-phase adsorption/chemistry/diffusion.**
**Large range of length scales**
**Devices (1000 ********m)**
**Sulfide (1 ********m)**
**Oxide (0.01 ********m)**
**Water layer (0.001 ********m)**
**System level circuit model eventually predicts corrosion effects on system level response.**

**Numerous microscopic processes must be tracked.**

**Multi-phase reaction/diffusion continuum model is needed to realistically simulate corrosion**

**Continuum Simulation: ****Sulfide growth in pinhole**

## Issue is sub-critical crack growth driven by latent physical defects under mechanical stress, and environmental interactions (e.g., moisture)

Key preventative aspects include
Ensuring stress levels are below material-specific threshold
Ensuring stress intensity factor levels are below a material-specific threshold
Validating results of modeling using experimental approaches and model samples

Applicable to glass-to-metal seals, battery feed throughs, rack and panel connectors, alumina braze joints, glass ceramics, ferrite ceramics, and PZT

**Issue is sub-critical crack growth driven by latent physical defects under mechanical stress, and environmental interactions (e.g., moisture)**
** **
**Key preventative aspects include**
**Ensuring stress levels are below material-specific threshold**
**Ensuring stress intensity factor levels are below a material-specific threshold**
**Validating results of modeling using experimental approaches and model samples**
**Applicable to glass-to-metal seals, battery feed throughs, rack and panel connectors, alumina ****braze joints, glass ceramics, ferrite ceramics, and PZT**

**Material Aging Vulnerabilities: Ceramics**

## Degradation of Adhesives

**Molecular simulations to examine the nonequilibrium molecular mechanisms**

- Multscale calculations to predict the degradation & failure of adhesives

- Continuum methods to simulate crack initiation and propagation

- Simulation of competing failure types in polymer melt

- Crack surface in epoxy

**Notes:**
General note: There are  two main types of adhesives in the stockpile that we are treating, epoxies and silicones (or elastomers).
Epoxies are glassy, highly crosslinked networks. Silicones are rubbery, lightly crosslinked networks. The molecular scale dynamics of these
two systems is very different. The continuum scale dynamics is also different. Silicones are nonlinear viscoelastic.
Images: Left image is for 2D simulation of 3-point bend experiment. Right image is an electron micrograph of epon-versamid epoxy fracture.
Basically, the issue is adhesives are an integral part of the weapons and there are known issues (degradation, failure). 
Crack initiation and propagation are intrinsically multiscale and so is our approach. One of our general goals is to add the missing submicron physics to the fracture mechanics. 
Accomplishments: Previous MD work on epoxies treated adhesive failure. We now can & have done cohesive failure.
The rate-dependent fracture is result for the crack propagation code.

## Competitive Adsorptionand Reactions in Desiccants

**Issues**
- Desiccants in weapons systems show unanticipated adsorption behavior.
- Experiments to determine desiccant capacity and lifetime are difficult and costly.

**Approach**
- Requires multiscale approach to understand fundamental response of materials.
- Use quantum mechanics to investigate chemical interactions inside desiccants and  barriers to adsorption due to cations.
- Eventually requires understanding of effects at system level.

- Simulation of chemical reactions in nanoporous desiccant

- Adsorption on the surface of desiccant

**Notes:**
[JBA: Need customer/user ID.]
[JBA: Approach should indicate modeling approach.  It appears to be atomistic, but what atomistic method(s)?  What is sequence of atomistic configurations or simulations to be pursued?  How might these simulations lead to a predictive model of dessiccant performance?]
[JBA: Does “Models” under Accomplishments mean atomistic configurations have been generated or does it mean the “models” refered to in the Objective have been created?]
[JBA: Need explanation of the challenge faced in contructing good representations (don’t keep using “model” with multiply meanings) of zeolite and molecules.]

## Roadmap for Aging and Radiation Effects

**Issues**
- Helium (He) is a by-product of the decay of tritium.  He bubbles grow as the concentration of He increases and eventually cause failure.
- Need to identify criteria for predicting the accelerated release of He from implanted metals and aged hydrides.
- Could eventually be important to the H2 economy

_**Material strength & defect content**_
******, ********, (********/ p), ******** as functions **
**of {He/M, H/M, T, R, L}**

**first principles**
**calculations**

## Summary

**Developing predictive aging models represents many challenges:**
**Developing fundamental understanding of underlying physics**
**Validating theories with relevant data**
**Linking materials processes to materials aging**
**Novel algorithms and computational tools that allow for long life time simulations**
**	**