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Failure and Design
Published in Zainul Huda, Metallurgy for Physicists and Engineers, 2020
The Larson-Miller parameter (LMP) is a useful tool for determining the stress-rupture time or creep life for any stress-temperature combination for a given material. It is defined as: LMP=T1000(C+logt)where LMP is the Larson-Miller parameter; T is the temperature in Rankin, R; t is the creep-life time, h; and C is a material’s constant (usually C=20). Figure 10.11 shows the LMPs for Ti alloys. Creep lives for most materials lie in the range of 100–100,000 h (see Example 10.18).
Fracture Mechanisms in Nonmetals
Published in T.L. Anderson, Fracture Mechanics, 2017
where tT and tTo are the times to reach a specific modulus at temperatures T and To, respectively, To is a reference temperature (usually defined at Tg), and C1 and C2 are fitting parameters that depend on material properties. Equation 6.5, which is known as the WLF relationship, typically is valid in the range Tg < T < Tg + 100°C. Readers familiar with creep in metals may recognize an analogy with the Larson–Miller parameter [8], which assumes a time–temperature equivalence for creep rupture.
Development and Validation of Thermal-Mechanical Creep Failure Module for Reactor Pressure Vessel Lower Head
Published in Nuclear Science and Engineering, 2023
Hao Yang, Bin Zhang, Pengcheng Gao, Runze Zhai, Jianqiang Shan
ISAA is an integrated severe accident analysis program independently developed by Xi’an Jiaotong University that can be used to simulate the severe accident process of nuclear reactors.21 ISAA follows a modular approach in which different modules are integrated into ISAA to model the physical phenomena during severe accidents.22 The mechanical model of ISAA’s lower head adopts a simple film stress calculation relationship and judges the failure of the lower head only by the melting point and creep of the material. ISAA’s LHF model is based on the Larson-Miller parameter (LMP) criterion and uses the calculated temperature distribution of the lower head to predict creep. It uses the simplified effective membrane stress and the mass average temperature of the lower head to calculate the LMP of each node. At the time of failure, it is generally believed that the strain of the lower head is between 20% and 30% (Ref. 9), but ISAA conservatively believes that failure will occur when the strain reaches 18%. The stress calculated by ISAA is unevenly distributed on the wall of the lower head along the thickness direction, which is inconsistent with the actual situation, and the change in the wall thickness of the lower head is not considered.
Fractional-order model for temperature-dependent rheological behaviors of polymeric materials
Published in Mechanics of Advanced Materials and Structures, 2023
Wei Cai, Ping Wang, Yongqi Zhang
A viscoelastic model has been proposed to reveal the temperature-sensitive creep evolutions of graphene-polymer nanocomposites [17]. Moreover, power-law models have been adopted to capture temperature-dependent creep behaviors, such as the modified power-law model [18], and the famous Miller-Norton formula [19]. Additionally, Larson-Miller parameter is utilized to explore the effects of temperature on creep life of PMMA [20]. In terms of stress relaxations, long-term relaxation behaviors of rubber materials in low temperature environment are evaluated by the well-known time-temperature superposition method [21]. Recently, Duan et al. have proposed a constitutive model to predict temperature-sensitive stress-relaxation responses of polymeric composites [22]. A three network constitutive model (TNM) has been developed to investigate thermal-influenced tensile or compressive relaxation modulus of HDPE [23].
Prediction of metal temperature by microstructural features in creep exposed austenitic stainless steel with sparse modeling
Published in Science and Technology of Advanced Materials: Methods, 2021
Akihiro Endo, Kota Sawada, Kenji Nagata, Hideki Yoshikawa, Hayaru Shouno
Specifically, the features of the precipitate region determined from an optical micrograph can be calculated via image processing, and the regression to the Larson-Miller parameter (LMP), which is generally used in creep strength evaluation, considered by using these features as the inputs. The features obtained from the precipitate area include the size, area fraction, and number density of the precipitates. Based on the previous studies [4] that analyzed the correlation between these features and the LMP, the precipitate feature is considered to be crucial for estimating the metal temperature. However, multivariate analysis of such features has not been well discussed. In this study, a comprehensive set of image statistical parameters such as area, contour, circularity, and distance between the regions, is constructed for a total of 38 feature dimensions. These features are considered to be important for the LMP regression, and the framework of sparse modeling is established. To verify the effectiveness of the proposed method, it is applied to KA-SUS304J1HTB (18Cr-9Ni-3Cu-Nb-N steel), reported in the NIMS Creep Data Sheets No. 56A [6] and No. M-11 [1], to analyze the fitting and prediction performances. Consequently, this study successfully predicts the temperature for unknown data with an error within °C.