2017
DOI: 10.1016/j.jenvrad.2017.04.016
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Enhanced air dispersion modelling at a typical Chinese nuclear power plant site: Coupling RIMPUFF with two advanced diagnostic wind models

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Cited by 26 publications
(5 citation statements)
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“…The K′ and n′ derived from two temperatures are listed in Table 2. It could be seen that the cyclic strength coefficient and the cyclic hardening exponent are much bigger compared (1)…”
Section: Cyclic Stress-strain Behavior and Fatigue Lifementioning
confidence: 99%
See 1 more Smart Citation
“…The K′ and n′ derived from two temperatures are listed in Table 2. It could be seen that the cyclic strength coefficient and the cyclic hardening exponent are much bigger compared (1)…”
Section: Cyclic Stress-strain Behavior and Fatigue Lifementioning
confidence: 99%
“…In the past few years, nuclear power plant has underwent a rapid development as its extraordinary high efficiency, reliable security as well as so-called environmental friendly facilities [1,2]. As the most critical component of the turbines, the long term reliability of the rotor must be ensured due to the ultra-severe working conditions, including the long time thermal exposure, the complex cyclic loading and even the probable neutron damage [3][4][5][6].…”
Section: Introductionmentioning
confidence: 99%
“…The gas dispersion model can be used not only in regional ecological monitoring and environmental pollution control but also in emergency response management. The research and applications of the gas dispersion model in the emergency field mainly concentrate on the nuclear accident (Liu et al, 2017;Li et al, 2018;Ulimoen et al, 2022), chemical industrial park (Huang et al, 2019;Cheng et al, 2021), gas pipeline (Mishra et al, 2015 CFD;Yan et al, 2016), and road transportation (Fallah-Shorshani et al, 2015;Kota et al, 2013). Compared with the environmental assessment cases, the emergency simulation often requires a near-field, finer spatial-temporal computational grid and further analysis and adjustment of the model parameters (Rzeszutek, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…Huang S. X. et al (2018) proposed a pollution source dynamic inversion and grid quantitative traceability algorithm based on CAMx numerical prediction model, which can quickly and quantitatively trace the emission source and contribution rate of air pollution in the next 7 days. Liu Y. et al (2017 used 4DVAR Cost Gradient Function and Truncated Total Teast Squares (TTLS-VAR) to regularize and correct the error between diffusion prediction operator and observation value, which reduced the error of inversion algorithm and improved the accuracy compared with the basic 4DVAR algorithm. Hassan et al (2020) proposed a quantitative traceability algorithm combining improved variational iteration and Laplace transform, and applied it to the inverse solution of fractional nonlinear convection-diffusion equation for the first time.…”
Section: Introductionmentioning
confidence: 99%