High-Fidelity Coupling of Predictive Plasma-Wall Models Goal: - - PowerPoint PPT Presentation

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Oct 27, 2023 530 likes •589 views

High-Fidelity Coupling of Predictive Plasma-Wall Models Goal: Develop a predictive model of the plasma-wall interaction Mitchell Walker Professor Georgia Institute of Technology Michael Keidar Julian Rimoli Professor Associate Professor

SLIDE 1

High-Fidelity Coupling of Predictive Plasma-Wall Models

Michael Keidar Professor George Washington University Julian Rimoli Associate Professor Georgia Institute of Technology Mitchell Walker Professor Georgia Institute of Technology

July 1, 2018 Goal: Develop a predictive model of the plasma-wall interaction

SLIDE 2

visibility

Ion energy deposition is interpolated using the “front” and the “groove” data as a function of the visibility and the size factor of the surface ripples

I. Schweigert, "2018 Plasma Sources Sci. Technol.

27 045004

Energy bounds from plasma models Joint probability of erosion function M e c h a n i c a l d a m a g e I

e n e r g y d e p

i t i

Size factor Results The model is based on the probability of the surface of being eroded. The Probability of material removal depends on: ü Mechanical damage ü Ion energy deposition Surface evolution Ion energy deposition to wall Model

Erosion Prediction: Algorithm and Program

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Plasma Modeling: Non-uniformity of ion flux distribution over segmented surface

Task: Model the ion current profile near plane BN surface with segments of material with low secondary electron emission (SEE). Model: Boltzmann eqns for electron and ion distribution functions, Poisson eqn for electrical potential. Emissive floating a) plane

segmented or b) grooved BN plates is 28 cm from the cathode. (2D PIC MCC with code PlasmaNov.)

Segmented surface: Potential distribution before and after transition : U= - 190 V Segmented surface: Ion current density profile over z for U=-150 V (blue)

ver x for

U=-190 V (red).

Conclusions: For segmented surface (BN surface with low SEE insert) as well as for grooved surface, the near-surface potential is non-monotonic. It redistributes ions current inside of grooves. Etching rate of at low SEE segments and inside of grooves has higher rate that at front BN surface.

For comparison: Potential over grooved surface, white lines show electron trajectories Segmented surface: Potential distribution before and after transition : U= - 150 V

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Impact of Cracks on the Erosion

Proposed mechanism: Expansion/compression of BN into the surrounding silica matrix during thermal cycles Impact on erosion process: Preferential sputtering of BN compared to silica in regions of high plasma flux Quantify the effect of thermal cycling on crack formation in anisotropic material (M26 borosil)

1. What size cracks can be formed by thermal shock of M26 borosil? Can the cracks grow

beyond the grain boundaries?

2. Are stresses from thermal cycling solely responsible for the microcracks?

Initial: 3D height maps taken after 30 thermal cycles at 100x magnification with confocal

microscope. No cracks detected – now

moving to SEM for microcrack detection

SEM image of unpolished borosil

First high-res SEM images taken of borosil without prior coating (collaboration with SEM experts)

Future: Does the presence of cracks play a role in the plasma erosion process?

Step 1: Develop diagnostic Step 2: Software for crack identification Flood-fill algorithm to identify crack boundaries from a single user click in the interior of a crack