低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性...低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性计算方法,并通过数值算例证明了该方法的准确性和高效性。以某型柴油发动机活塞组结构为研究对象,基于热-机耦合分析建立活塞有限元模型,综合考虑关键尺寸、材料属性及载荷的不确定性,运用该方法对活塞进行了低周疲劳可靠性分析。可靠性分析结果表明,与同类型方法相比,该方法计算效率更高,仅需要有限元计算20+93次,当活塞的期望设计寿命为1.4×10^(4)时,其疲劳失效概率为1.053%;灵敏度分析结果表明,活塞高度、活塞直径、材料弹性模量和疲劳计算模型参数对可靠性的影响较大,分析结果可为活塞的可靠性设计提供指导。展开更多
The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount ...The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.展开更多
为研究基础结构的材料属性和尺寸对单桩式海上风机基础可靠性的影响,提出基于PC-Kriging模型(Polynomial-Chaos-based Kriging,PC-Kriging)和蒙特卡洛模拟(Monte Carlo Simulation,MCS)方法,结合IEGO学习函数建立的单桩式海上风机基础...为研究基础结构的材料属性和尺寸对单桩式海上风机基础可靠性的影响,提出基于PC-Kriging模型(Polynomial-Chaos-based Kriging,PC-Kriging)和蒙特卡洛模拟(Monte Carlo Simulation,MCS)方法,结合IEGO学习函数建立的单桩式海上风机基础可靠性分析模型,并通过算例验证了该方法的精确性。以50年重现期的海况为极端环境,考虑材料密度、弹性模量和桩腿壁厚的不确定性,进行单桩式海上风机基础在塔筒顶部位移和应力控制两个失效因素下的可靠性分析,并进行全局灵敏度分析。分析结果表明,单桩式海上风机基础失效概率为8.4×10-3,材料密度对可靠性影响可以忽略不计,而材料弹性模量和桩腿壁厚对可靠性影响较大。展开更多
文摘低周疲劳是发动机活塞的典型失效模式,为研究多源不确定性因素对活塞低周疲劳可靠性的影响,提高可靠性分析效率,基于Polynomial-Chaos-based Kriging(PC-Kriging)模型和蒙特卡洛模拟(Monte Carlo Simulation,MCS),构建了一种新的可靠性计算方法,并通过数值算例证明了该方法的准确性和高效性。以某型柴油发动机活塞组结构为研究对象,基于热-机耦合分析建立活塞有限元模型,综合考虑关键尺寸、材料属性及载荷的不确定性,运用该方法对活塞进行了低周疲劳可靠性分析。可靠性分析结果表明,与同类型方法相比,该方法计算效率更高,仅需要有限元计算20+93次,当活塞的期望设计寿命为1.4×10^(4)时,其疲劳失效概率为1.053%;灵敏度分析结果表明,活塞高度、活塞直径、材料弹性模量和疲劳计算模型参数对可靠性的影响较大,分析结果可为活塞的可靠性设计提供指导。
基金supported by the National Key R&D Program of China(No.2021YFB1715000)the National Natural Science Foundation of China(No.52375073)。
文摘The Reliability-Based Design Optimization(RBDO)of complex engineering structures considering uncertainties has problems of being high-dimensional,highly nonlinear,and timeconsuming,which requires a significant amount of sampling simulation computation.In this paper,a basis-adaptive Polynomial Chaos(PC)-Kriging surrogate model is proposed,in order to relieve the computational burden and enhance the predictive accuracy of a metamodel.The active learning basis-adaptive PC-Kriging model is combined with a quantile-based RBDO framework.Finally,five engineering cases have been implemented,including a benchmark RBDO problem,three high-dimensional explicit problems,and a high-dimensional implicit problem.Compared with Support Vector Regression(SVR),Kriging,and polynomial chaos expansion models,results show that the proposed basis-adaptive PC-Kriging model is more accurate and efficient for RBDO problems of complex engineering structures.
文摘为研究基础结构的材料属性和尺寸对单桩式海上风机基础可靠性的影响,提出基于PC-Kriging模型(Polynomial-Chaos-based Kriging,PC-Kriging)和蒙特卡洛模拟(Monte Carlo Simulation,MCS)方法,结合IEGO学习函数建立的单桩式海上风机基础可靠性分析模型,并通过算例验证了该方法的精确性。以50年重现期的海况为极端环境,考虑材料密度、弹性模量和桩腿壁厚的不确定性,进行单桩式海上风机基础在塔筒顶部位移和应力控制两个失效因素下的可靠性分析,并进行全局灵敏度分析。分析结果表明,单桩式海上风机基础失效概率为8.4×10-3,材料密度对可靠性影响可以忽略不计,而材料弹性模量和桩腿壁厚对可靠性影响较大。
