This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are...This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.展开更多
Turbulent nonpremixed CH4/H2 flame has been simulated using several typical differential secondmoment turbulence closure (SMTC) models. To clarify the applicability of the various models, the LRR-IP model,JM model, SS...Turbulent nonpremixed CH4/H2 flame has been simulated using several typical differential secondmoment turbulence closure (SMTC) models. To clarify the applicability of the various models, the LRR-IP model,JM model, SSG model as well as two modified LRR-IP models were tested. Some of above-mentioned SMTC models cannot provide the overall satisfactory predictions of this challenging case. It is confirmed again that the standard LRR-IP model considerably overpredict the centerline velocity decay rate, and therefore performs not well. Also it is interesting to observe that the JM model does not perform well in this challenging test case, although it has already been proved successful in other cases. The SSG model produces quite satisfactory prediction and performs equally well or better than the two modified LRR-IP models in the reacting case. It can be concluded that the modified LRR-IP models as well as the SSG model are superior to the other SMTC models in the turbulent nonpremixed CH4/H2 flame.展开更多
A second-moment closure for the near-wall turbulence is proposed. The limiting behaviour of this closure near a wall is consistent with that of the exact Reynolds-stress transport equations, and it converts asymptotic...A second-moment closure for the near-wall turbulence is proposed. The limiting behaviour of this closure near a wall is consistent with that of the exact Reynolds-stress transport equations, and it converts asymptotically into a high- Reynolds-number closure remote from the wall. The closure is applied to a pressure- driven 3D transient channel flow. The predicted results are in fair agreement with the DNS data.展开更多
为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,...为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。展开更多
基金supported by the National Natural Science Foundation of China(Grant Nos.52109144,52025094 and 52222905).
文摘This paper introduces a novel approach for parameter sensitivity evaluation and efficient slope reliability analysis based on quantile-based first-order second-moment method(QFOSM).The core principles of the QFOSM are elucidated geometrically from the perspective of expanding ellipsoids.Based on this geometric interpretation,the QFOSM is further extended to estimate sensitivity indices and assess the significance of various uncertain parameters involved in the slope system.The proposed method has the advantage of computational simplicity,akin to the conventional first-order second-moment method(FOSM),while providing estimation accuracy close to that of the first-order reliability method(FORM).Its performance is demonstrated with a numerical example and three slope examples.The results show that the proposed method can efficiently estimate the slope reliability and simultaneously evaluate the sensitivity of the uncertain parameters.The proposed method does not involve complex optimization or iteration required by the FORM.It can provide a valuable complement to the existing approximate reliability analysis methods,offering rapid sensitivity evaluation and slope reliability analysis.
文摘Turbulent nonpremixed CH4/H2 flame has been simulated using several typical differential secondmoment turbulence closure (SMTC) models. To clarify the applicability of the various models, the LRR-IP model,JM model, SSG model as well as two modified LRR-IP models were tested. Some of above-mentioned SMTC models cannot provide the overall satisfactory predictions of this challenging case. It is confirmed again that the standard LRR-IP model considerably overpredict the centerline velocity decay rate, and therefore performs not well. Also it is interesting to observe that the JM model does not perform well in this challenging test case, although it has already been proved successful in other cases. The SSG model produces quite satisfactory prediction and performs equally well or better than the two modified LRR-IP models in the reacting case. It can be concluded that the modified LRR-IP models as well as the SSG model are superior to the other SMTC models in the turbulent nonpremixed CH4/H2 flame.
基金The project supported by the National Natural Science Foundation of China
文摘A second-moment closure for the near-wall turbulence is proposed. The limiting behaviour of this closure near a wall is consistent with that of the exact Reynolds-stress transport equations, and it converts asymptotically into a high- Reynolds-number closure remote from the wall. The closure is applied to a pressure- driven 3D transient channel flow. The predicted results are in fair agreement with the DNS data.
文摘为进一步提高温度业务预报水平,本文采用美国国家环境预报中心环境模式中心(National Centers for Environmental Prediction-Environmental Modeling Center,NCEP-EMC)研发的基于递归贝叶斯模型过程(recursive Bayesian model process,RBMP)的多模式集合技术,开展了华东2 m温度预报试验。利用2016—2017年欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)、NCEP和加拿大气象中心(Canadian Meteorological Centre,CMC)3个具有代表性的全球集合预报系统产品,在对各模式进行偏差订正的基础上,开展了RBMP算法应用试验和评估,建立了华东地区应用方案,再利用2019年9月—2020年5月ECMWF、NCEP集合预报资料开展试运行,初步讨论了RBMP方法在冬春季节预报失败案例中的适用性。结果表明:RBMP方法能够提供更加可靠的概率预报分布并有效提高短期时效的预报技巧。其中,冬季改进最明显,集合平均的均方根误差比ECMWF订正预报和等权重多模式集合分别降低3.0%~10.5%和2.0%~5.0%,且对高温和低温事件均具有更优的分辨能力。此外,RBMP方法还能够提高大部分预报失败案例的预报准确率,为难报案例提供了有价值的不确定信息。总体而言,RBMP技术不仅保留了BMA(Bayesian model averaging)方法的优势,且能满足业务应用对资料存储和计算效率的需求,通过二阶矩调整可以有效校正集合离散度,为进一步提高短期温度预报技巧提供了一种思路。
