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吴东方, Dongfang
AIChE Journal October 2007, Vol. 53, No. 10,-0001,():
-1年11月30日
Mechanical
solid
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吴东方, D.
Trans IChemE, Part A, December 2006, Chemical Engineering Research and Design, 84 (A12): 1152-1157,-0001,():
-1年11月30日
The mechanical strength of solid catalysts is distributed over a wide range of values. The suitability of the normal, lognormal and Weibull distributions to model the catalyst strength variation was judged by three goodness-of-fit criteria: the coefficient of determination, Akaike information criterion and Kolmogorov–Smirnov test. It is concluded that the Weibull distribution is most universal to represent the catalyst strength data, though sometimes it may not be the optimal candidate. It is elucidated that the catalyst strength variation results from the brittle fracture nature of the mechanical failure of solid catalyst materials. The low-strength/probability part of the catalyst strength distribution is the key domain for the mechanical reliability, while the mean strength that has traditionally been taken as a measure of the catalyst strength is of less importance. It is also pointed out that a mechanical strength distributed in a narrow window, i.e., with a high Weibull modulus, is beneficial to industrial applications of solid catalysts.
mechanical
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吴东方, Dongfang
J Mater Sci (2006) 41: 5630-5638,-0001,():
-1年11月30日
A Monte Carlo simulation is used to obtain the statistical properties of the Weibull parameters estimated by the linear regression, weighted linear regression, maximum likelihood and moments methods, respectively. Results reveal that the estimated Weibull modulus is always biased, which has a much lower accuracy than the scale parameter. The mean square error is adopted as a criterion for the comparison of the estimation methods. It is shown that both the probability estimators and the weight factors have great effects on the estimation precision of the Weibull modulus. The weighted linear regression with a weight factor ofWi=3.3Pi –27.5[1–(1–Pi)0.025] and a probability estimator of Pi=(i–0.3)/(n+0.4) gives the most accurate estimation for sample sizes of 9–52. The maximum likelihood method recommended for any sample size by previous authors, comes first only for sample sizes larger than or equal to 53; furthermore, it is less conservative than the regression methods, and hence results in a lower safety in reliability predictions.
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吴东方, Dongfang
Journal of the European Ceramic Society 26 (2006) 1099-1105,-0001,():
-1年11月30日
Monte Carlo simulations were used to search for the probability estimator for the unbiased estimate of the Weibull parameters in the linear regression method. Compared with commonly-used probability estimators, the estimator proposed gives a more accurate estimation of the Weibull modulus and the same estimation precision of the scale parameter. It is found that the estimator proposed is more conservative than the estimator Pi = (i−0.5)/n recommended by previous authors, and hence results in a higher safety in reliability predictions. The unbiased properties of the estimated Weibull parameters were validated with actual experimental data. It is also concluded that the estimated Weibull modulus from actual experimental data is more dispersive than that from Monte Carlo simulation, which arises from the fact that the strength data from actual experiments does not perfectly follow the Weibull statistics.
Fracture,
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吴东方, Dongfang
Part. Part. Syst. Charact. 22 (2005) 63-68,-0001,():
-1年11月30日
The
catalyst,
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