Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization ...Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.展开更多
To avoid the high computational cost and much modification in the process of applying traditional reliability-based design optimization method, a new reliability-based concurrent subspace optimization approach is prop...To avoid the high computational cost and much modification in the process of applying traditional reliability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and reliability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.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National Natural Science Foundation of China (10377015)
文摘Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidisciplinary design optimization （MDO） techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.
基金the Nationa Natural Science Foundation of China (Grant No. 10377015)
文摘To avoid the high computational cost and much modification in the process of applying traditional reliability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and reliability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.
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。
