Updated band model parameters for H2O, CO2, CH4 and CO radiation at high temperature

2012 ◽  
Vol 55 (13-14) ◽  
pp. 3349-3358 ◽  
Author(s):  
Philippe Rivière ◽  
Anouar Soufiani
2017 ◽  
Vol 46 (7) ◽  
pp. 704001
Author(s):  
蔡红华 Cai Honghua ◽  
聂万胜 Nie Wansheng ◽  
吴 睿 Wu Rui ◽  
苏凌宇 Su Lingyu ◽  
侯志勇 Hou Zhiyong

1978 ◽  
Vol 44 (381) ◽  
pp. 1616-1623
Author(s):  
Takeshi KUNITOMO ◽  
Masato OSUMI ◽  
Susumu UEOKA ◽  
Hirohisa MASUZAKI ◽  
Koichi UTSUNOMIYA

Author(s):  
Felix Koelzow ◽  
Muhammad Mohsin Khan ◽  
Christian Kontermann ◽  
Matthias Oechsner

Abstract Several (accumulative) lifetime models were developed to assess the lifetime consumption of high-temperature components of steam and gas turbine power plants during flexible operation modes. These accumulative methods have several drawbacks, e.g. that measured loading profiles cannot be used within accumulative lifetime methods without manual corrections, and cannot be combined directly to sophisticated probabilistic methods. Although these methods are widely accepted and used for years, the accumulative lifetime prediction procedures need improvement regarding the lifetime consumption of thermal power plants during flexible operation modes. Furthermore, previous investigations show that the main influencing factor from the materials perspective, the critical damage threshold, cannot be statistically estimated from typical creep-fatigue experiments due to massive experimental effort and a low amount of available data. This paper seeks to investigate simple damage mechanics concepts applied to high-temperature components under creep-fatigue loading to demonstrate that these methods can overcome some drawbacks and use improvement potentials of traditional accumulative lifetime methods. Furthermore, damage mechanics models do not provide any reliability information, and the assessment of the resultant lifetime prediction is nearly impossible. At this point, probabilistic methods are used to quantify the missing information concerning failure probabilities and sensitivities and thus, the combination of both provides rigorous information for engineering judgment. Nearly 50 low cycle fatigue experiments of a high chromium cast steel, including dwell times and service-type cycles, are used to investigate the model properties of a simple damage evolution equation using the strain equivalence hypothesis. Furthermore, different temperatures from 300 °C to 625 °C and different strain ranges from 0.35% to 2% were applied during the experiments. The determination of the specimen stiffness allows a quantification of the damage evolution during the experiment. The model parameters are determined by Nelder-Mead optimization procedure, and the dependencies of the model parameters concerning to different temperatures and strain ranges are investigated. In this paper, polynomial chaos expansion (PCE) is used for uncertainty propagation of the model uncertainties while using non-intrusive methods (regression techniques). In a further post-processing step, the computed PCE coefficients of the damage variable are used to determine the probability of failure as a function of cycles and evolution of the probability density function (pdf). Except for the selected damage mechanics model which is considered simple, the advantages of using damage mechanics concepts combined with sophisticated probabilistic methods are presented in this paper.


Author(s):  
Hailong Chen ◽  
Yile Hu ◽  
Benjamin W. Spencer

In this paper, reformulation of classical bond-based peridynamic thermomechanical model for irregular domain decomposition and its MOOSE-based implicit formulation are presented. First, the irregular grid based peridynamic thermomechanical model is formulated and model parameters are derived. Following this, an implicit formulation for the solution of static or quasi-static problems is presented. Some aspects of the MOOSE-based implementation are given. After that, the formulation is verified against benchmark solutions for thermomechanic problems. Crack initiation and propagation in circular (2D) and cylindrical (3D) nuclear fuels at high temperature are studied using irregular grids.


Author(s):  
Se´bastien Depraz ◽  
Philippe Rivie`re ◽  
Marie-Yvonne Perrin ◽  
Anouar Soufiani

A statistical narrow-band model is developed for the optically non-thin electronic systems of carbonaceous molecules in CO2-N2 plasmas and its accuracy is studied under equilibrium and non-equilibrium conditions. Line by line calculations are used to produce curves of growth of transmissivities from which band model parameters are calculated by least-square adjustments. The model is shown to provide quite accurate description of radiative properties and radiative intensities for Doppler, Lorentz, and Voigt line profiles, and for both local thermodynamic equilibrium and a multi-temperature description of the gas mixture thermodynamic state. The model is also suitable for a more general description of the gas thermodynamic state where the electronic state populations are arbitrary.


Author(s):  
Aritra Chakraborty ◽  
M. C. Messner ◽  
T.-L. Sham

Abstract Calibrating inelastic models for high temperature materials used in advanced reactor heat exchangers is a critical aspect in accurately predicting their deformation behavior under different loading conditions, and thus determining the corresponding failure times. The experimental data against which these models are calibrated often contains a wide degree of variability caused by heat-to-heat material property variations and general experimental uncertainty. Most often, model calibration is done against mean of these experimental data without considering this variability. In this work we aim to capture the bounds of the viscoplastic parameter uncertainties that enclose this observed scatter in the experimental data using Bayesian Markov Chain Monte Carlo (MCMC) methods. Bayesian inference provides a probabilistic framework that allows to coherently quantify parameter uncertainties based on some prior parameter distributions and the available data. To perform the statistical Bayesian MCMC analysis, a pre-calibrated model, fitted against mean of the experimental data, is used as an initial guess for the prior distribution and bounds, while further sampling is done using Meteropolis–Hastings algorithm for four Markov chains in tandem, to finally obtain the posterior distribution of the model parameters. Since different inelastic parameters are sensitive to different tests, data from multiple experimental conditions (tensile, and creep) are combined to capture the bounds in all the parameters. The developed statistical model reasonably captures the scatter observed in the experimental data. Quantifying uncertainty in inelastic models will improve high temperature engineering design practice and lead to safer, more effective component designs.


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