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Title: Automated Damage Onset Analysis Techniques Applied to KDP Damage and the Zeus Small Area Damage Test Facility

Abstract

Automated damage testing of KDP using LLNL's Zeus automated damage test system has allowed the statistics of KDP bulk damage to be investigated. Samples are now characterized by the cumulative damage probability curve, or S-curve, that is generated from hundreds of individual test sites per sample. A HeNe laser/PMT scatter diagnostic is used to determine the onset of damage at each test site. The nature of KDP bulk damage is such that each scatter signal may possess many different indicators of a damage event. Because of this, the determination of the initial onset for each scatter trace is not a straightforward affair and has required considerable manual analysis. The amount of testing required by crystal development for the National Ignition Facility (NIF) has made it impractical to continue analysis by hand. Because of this, we have developed and implemented algorithms for analyzing the scatter traces by computer. We discuss the signal cleaning algorithms and damage determination criteria that have lead to the successful implementation of a LabView based analysis code. For the typical R/1 damage data set, the program can find the correct damage onset in more than 80% of the cases, with the remaining 20% being left to operatormore » determination. The potential time savings for data analysis is on the order of {approx} 100X over manual analysis and is expected to result in the savings of at least 400 man-hours over the next 3 years of NIF quality assurance testing.« less

Authors:
;
Publication Date:
Research Org.:
Lawrence Livermore National Lab., CA (US)
Sponsoring Org.:
USDOE Office of Defense Programs (DP) (US)
OSTI Identifier:
791053
Report Number(s):
UCRL-JC-134765
TRN: US0301500
DOE Contract Number:  
W-7405-Eng-48
Resource Type:
Conference
Resource Relation:
Conference: 31st Boulder Damage Symposium: Annual Symposium on Optical Materials for High Power Lasers, Boulder, CO (US), 10/04/1999--10/07/1999; Other Information: PBD: 16 Dec 1999
Country of Publication:
United States
Language:
English
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY; ALGORITHMS; CLEANING; DATA ANALYSIS; IMPLEMENTATION; LASERS; PROBABILITY; QUALITY ASSURANCE; STATISTICS; TESTING; US NATIONAL IGNITION FACILITY

Citation Formats

Sharp, R, and Runkel, M. Automated Damage Onset Analysis Techniques Applied to KDP Damage and the Zeus Small Area Damage Test Facility. United States: N. p., 1999. Web.
Sharp, R, & Runkel, M. Automated Damage Onset Analysis Techniques Applied to KDP Damage and the Zeus Small Area Damage Test Facility. United States.
Sharp, R, and Runkel, M. Thu . "Automated Damage Onset Analysis Techniques Applied to KDP Damage and the Zeus Small Area Damage Test Facility". United States. https://www.osti.gov/servlets/purl/791053.
@article{osti_791053,
title = {Automated Damage Onset Analysis Techniques Applied to KDP Damage and the Zeus Small Area Damage Test Facility},
author = {Sharp, R and Runkel, M},
abstractNote = {Automated damage testing of KDP using LLNL's Zeus automated damage test system has allowed the statistics of KDP bulk damage to be investigated. Samples are now characterized by the cumulative damage probability curve, or S-curve, that is generated from hundreds of individual test sites per sample. A HeNe laser/PMT scatter diagnostic is used to determine the onset of damage at each test site. The nature of KDP bulk damage is such that each scatter signal may possess many different indicators of a damage event. Because of this, the determination of the initial onset for each scatter trace is not a straightforward affair and has required considerable manual analysis. The amount of testing required by crystal development for the National Ignition Facility (NIF) has made it impractical to continue analysis by hand. Because of this, we have developed and implemented algorithms for analyzing the scatter traces by computer. We discuss the signal cleaning algorithms and damage determination criteria that have lead to the successful implementation of a LabView based analysis code. For the typical R/1 damage data set, the program can find the correct damage onset in more than 80% of the cases, with the remaining 20% being left to operator determination. The potential time savings for data analysis is on the order of {approx} 100X over manual analysis and is expected to result in the savings of at least 400 man-hours over the next 3 years of NIF quality assurance testing.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {1999},
month = {12}
}

Conference:
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