Variation of Beremin Model Parameters With Temperature by Monte Carlo Simulation

被引：5

作者：

Bhattacharyya, K. ^{[1
]}

Acharyya, S. ^{[1
]}

Dhar, S. ^{[1
]}

Chattopadhyay, J. ^{[2
]}

机构：

[1] Jadavpur Univ, Dept Mech Engn, 188 Raja SC Mallick Rd, Kolkata 700032, W Bengal, India

[2] Bhabha Atom Res Ctr, Reactor Safety Div, Mumbai 400085, Maharashtra, India

来源：

JOURNAL OF PRESSURE VESSEL TECHNOLOGY-TRANSACTIONS OF THE ASME | 2019年 / 141卷 / 02期

关键词：

PLASTIC FRACTURE-TOUGHNESS; BRITTLE TRANSITION REGION; SPECIMEN GEOMETRY; CLEAVAGE FRACTURE; LOCAL APPROACH; DUCTILE; METHODOLOGY;

D O I：

10.1115/1.4042121

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

In this work, variation of the Beremin parameters with temperature for reactor pressure vessel material 20MnMoNi55 steel is studied. Beremin model is used, including the effect of plastic strain as originally formulated in the Beremin model. A set of six tests are performed at a temperature of -110 degrees C in order to determine reference temperature (T-0) and master curve for the entire ductile-to-brittle transition (DBT) region as per the ASTM Standard E1921. Monte Carlo simulation is employed to produce a large number of 1 T three-point bending specimen (TPB) fracture toughness data randomly drawn from the scatter band obtained from the master curve, at different temperatures of interest in the brittle dominated portion of DBT region to determine Beremin model parameters variation with temperatures.

引用

页数：7

共 50 条

[41] On Identification of Leaky Pipeline Parameters Via Monte Carlo Simulation
Moustafa, Kamal A. F.
Emam, Samir A.
Haik, Yousef S.
PROCEEDINGS OF THE ASME PRESSURE VESSELS AND PIPING CONFERENCE (PVP-2011), VOL 7, 2012, : 109 - 112
[42] Model replication techniques for parameter-influence studies and Monte Carlo simulation with random parameters
Korn, GA
MATHEMATICS AND COMPUTERS IN SIMULATION, 2005, 67 (06) : 501 - 513
[43] Recommended direct simulation Monte Carlo collision model parameters for planetary entry and related applications
Gosma, M.
Gopalan, K. Swaminathan
Subramaniam, S.
Stephani, K.
PHYSICS OF FLUIDS, 2025, 37 (03)
[44] Sensitivity of DNA Damage to Variance of Simulation Parameters in Microscopic Monte Carlo Simulation
Chi, Yujie
Lai, Youfang
Yan, Congchong
Tsai, Min-yu
Jia, Xun
BIOPHYSICAL JOURNAL, 2020, 118 (03) : 301A - 301A
[45] Investigation on Beremin parameters based on the toughness scaling model
Pan, Jianhua
Yin, Mengle
ENGINEERING FRACTURE MECHANICS, 2021, 247
[46] Determining measurement variation of lens parameters using Monte Carlo simulations
Ligthart, WCM
METROLOGY, INSPECTION, AND PROCESS CONTROL FOR MICROLITHOGRAPHY XIII, PTS 1 AND 2, 1999, 3677 : 661 - 668
[47] Monte - Carlo Simulations: Permeability Variation with Fractal and Pore Structural Parameters
Vadapalli, U.
JOURNAL OF INDIAN GEOPHYSICAL UNION, 2016, : 59 - 63
[48] SIMULATION BY MONTE CARLO
REED, R
TAPPI, 1966, 49 (01): : 28 - &
[49] Monte Carlo evaluation of biological variation: Random generation of correlated non-Gaussian model parameters
Hertog, Maarten L. A. T. M.
Scheerlinck, Nico
Nicolai, Bart M.
JOURNAL OF COMPUTATIONAL AND APPLIED MATHEMATICS, 2009, 223 (01) : 1 - 14
[50] On Monte Carlo simulation for the HJM model based on jump
Park, Kisoeb
Kim, Moonseong
Kim, Seki
COMPUTATIONAL SCIENCE - ICCS 2006, PT 1, PROCEEDINGS, 2006, 3991 : 38 - 45

← 1 2 3 4 5 →