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Krishnamurti Natesan - One of the best experts on this subject based on the ideXlab platform.
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Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment
Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems, 2019Co-Authors: Jae Phil Park, Subhasish Mohanty, Chi Bum Bahn, Saurin Majumdar, Krishnamurti NatesanAbstract:Abstract In general, the fatigue life of a safety critical pressure Component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, Reactor Component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these Components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly Reactor Components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water Reactor water on the Reactor Component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. We assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/Component fatigue life.
Jae Phil Park - One of the best experts on this subject based on the ideXlab platform.
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Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment
Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems, 2019Co-Authors: Jae Phil Park, Subhasish Mohanty, Chi Bum Bahn, Saurin Majumdar, Krishnamurti NatesanAbstract:Abstract In general, the fatigue life of a safety critical pressure Component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, Reactor Component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these Components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly Reactor Components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water Reactor water on the Reactor Component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. We assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/Component fatigue life.
Sang Woo Kwon - One of the best experts on this subject based on the ideXlab platform.
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Optimization of Dissimilar Friction Welding and Creep Rupture Tests for Nuclear Reactor Component Materials
Key Engineering Materials, 2006Co-Authors: Yu Sik Kong, Sang Woo KwonAbstract:An experimental work of dissimilar friction welding was conducted using 15 mm diameter solid bar in copper alloy (Cu-1Cr-0.5Zr) to stainless steel (STS316L) for being used as fusion Reactor Component materials, not only to optimize the friction welding parameters, but also to investigate the elevated temperature tensile strength and creep rupture properties for the friction welded joints under the optimal welding conditions. The main friction welding parameters were selected to endure good quality welds on the basis of visual examination, tensile tests, Vickers hardness survey of the bond area and HAZ. For friction weld joining of copper alloy to stainless steel bars, the total upset increases lineally as increasing heating time. Optimal welding conditions were selected as follows: Rotational speed 2000rpm, friction pressure 80MPa, upsetting pressure 140MPa, heating time 2 second, upsetting time 5 second and total upset 13mm. The weld interface of dissimilar friction welded steel bars was mixed strongly. And also the creep properties and creep life prediction by Larson-Miller parameter method were presented at the elevated temperatures of 300, 400 and 500oC.
Mustika, Deni Mustika - One of the best experts on this subject based on the ideXlab platform.
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PENENTUAN IMPURITAS ZIRKALOI-2 DENGAN PELARUTAN CAMPURAN HF-HNO3 DAN CAMPURAN H2SO4-HF-H2O2 MENGGUNAKAN ALAT SPEKTROFOTOMETER SERAPAN ATOM
PIN Pengelolaan Instalasi Nuklir, 2020Co-Authors: Sholikhah, Mu’nisatun Sholikhah, Rahmiati Rahmiati, Mustika, Deni MustikaAbstract:ABSTRAK. PENENTUAN IMPURITAS ZIRKALOI-2 DENGAN PELARUTAN CAMPURAN ASAM FLUORIDA-ASAM NITRAT DAN CAMPURAN ASAM SULFAT-ASAM FLUORIDA- HIDROGEN PEROKSIDA MENGGUNAKAN ALAT SPEKTROFOTOMETER SERAPAN ATOM, telah dilakukan. Zirkaloi-2 adalah bahan komponen reaktor nuklir yang nilai unsur impuritasnya dijaga ketat dibawah batas yang dipersyaratkan, dan dapat diuji dengan Spektrometer Serapan Atom (SSA). Uji linieritas dan akurasi perlu dilakukan untuk mengetahui pengaruh matriks dan pelarut terhadap pengujian impuritas zirkaloi-2. Uji linieritas dan akurasi dilakukan pada analisis unsur Cd, Co, Ni, Mg dan Mn dengan membandingkan preparasi zirkaloi-2 campuran asam HF- HNO3 dan H2SO4-HF-H2O2 dengan metode adisi. Dari hasil uji komparasi linieritas dan akurasi disimpulkan bahwa analisis SSA untuk pengotor zirkaloi-2 unsur Cd, Co, Ni dan Mg yang dipreparasi dengan campuran HF:HNO3 memenuhi persyaratan validasi pengujian. Sedangkan analisis SSA untuk pengotor zirkaloi-2 unsur Mo pada zirkaloi-2 yang dipreparasi dengan campuran HF:HNO3, untuk konsentrasi di atas 50 mg/L belum memenuhi persyaratan. Analisis SSA untuk pengotor zirkaloi-2 unsur Ni yang dipreparasi dengan campuran H2SO4:HF:H2O2 memenuhi persyaratan validasi pengujian. Analisis SSA untuk pengotor zirkaloi-2 unsur Mg yang dipreparasi dengan campuran H2SO4:HF:H2O2, untuk konsentrasi di atas 50 mg/L belum memenuhi persyaratan. Sedangkan analisis SSA untuk pengotor zirkaloi-2 unsur Cd, Co dan Mo pada zirkaloi-2 yang dipreparasi dengan H2SO4:HF:H2O2 belum memenuhi persyaratan. Kata kunci : Zirkaloi-2, linieritas, akurasi, impuritas, spektrofotometer serapan atom (SSA) ABSTRACT. DETERMINATION OF ZIRCALLOY-2 IMPURITY BY DISSOLUTION USING NITRIC ACID-FLUORIDIC ACID MIXTURE AND SULPHURIC ACID-FLUORIDIC ACID- HYDROGEN PEROXIDE MIXTURE BY ATOMIC ABSORBTION SPECTROPHOTOMETER. Zircalloy-2 is a Nuclear Reactor Component material whose elemental impurity values are strictly maintained below the required limits, and can be tested with an Atomic Absorption Spectrophotometer (AAS). Linearity and accuracy tests need to be done to determine the effect of matrices and solvents on the zircalloy-2 impurity test. Linearity and accuracy tests wer
Subhasish Mohanty - One of the best experts on this subject based on the ideXlab platform.
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Weibull and Bootstrap-Based Data-Analytics Framework for Fatigue Life Prognosis of the Pressurized Water Nuclear Reactor Component Under Harsh Reactor Coolant Environment
Journal of Nondestructive Evaluation Diagnostics and Prognostics of Engineering Systems, 2019Co-Authors: Jae Phil Park, Subhasish Mohanty, Chi Bum Bahn, Saurin Majumdar, Krishnamurti NatesanAbstract:Abstract In general, the fatigue life of a safety critical pressure Component is estimated using best-fit fatigue life curves (S-N curves). These curves are estimated based on underlying in-air condition fatigue test data. The best-fitting approach requires a large safety factor to accommodate the uncertainty associated with large scatter in fatigue test data. In addition to this safety factor, Reactor Component fatigue life prognostics requires an additional correction factor that in general is also estimated deterministically. This additional factor known as the environmental correction factor Fen is to cater the effect of the harsh coolant environment that severely reduces the life of these Components. The deterministic Fen factor may also lead to further conservative estimation of fatigue life leading to unnecessary early retirement of costly Reactor Components. To address the above-mentioned issues, we propose a data-analytics framework which uses Weibull and Bootstrap probabilistic modeling techniques for explicitly quantifying the uncertainty/scatter associated with fatigue life rather than estimating the lives based on a best-fit based deterministic approach. We assume the proposed probabilistic approach would provide the first hand information for assessing the maximum and minimum effects of pressurized water Reactor water on the Reactor Component. In the discussed approach, in addition to the probabilistic fatigue curves, we suggest using a probabilistic environment correction factor Fen. We assume the probabilistic fatigue curve and Fen would capture the S-N data scatter associated with the bulk effect of material grades, surface finish, strain rate, etc. on the material/Component fatigue life.
