Adaptive surrogate model coupled with stochastic configuration network strategies for time-dependent reliability assessment

Time-dependent reliability assessment is crucial in enhancing product development economics and product performance sustainability throughout the lifecycle. It is still a challenge to accurately and efficiently evaluate the time-dependent reliability of engineering systems. This paper proposes a novel adaptive surrogate model method combining stochastic configuration network (SCN) and Kriging strategies to evaluate time-dependent reliability. SCN has accurate approximation ability and learning efficiency for strongly nonlinear systems that can overcome the conventional time-dependent reliability calculation, which is time-consuming and characterized by low accuracy. The proposed method first applies SCN to establish the response model of the performance function with respect to time and obtain the extreme value of the performance function. Then, Kriging is used to establish the extreme value model of the performance function with respect to the random variables based on the extreme value of performance function. The adaptive process considering the characteristics of random variables samples is adopted to update the extreme value model until the model meets the confidence target. Lastly, Monte Carlo simulation is employed for time-dependent reliability assessment based on the established extreme value model. Three example studies are used to demonstrate the effectiveness of the proposed approach for time-dependent reliability assessment.     

Optimization of the impeller of sand-ejecting fire extinguisher based on CFD-DEM simulations and Kriging model

To explore the optimal impeller of fire extinguishing equipment within a larger range, the Latin hypercube sampling and Kriging method was adopted to establish the response surface with two input factors (the blade number and the angle of inclination). The study found that a longer scattering distance can obtain a better fire-extinguishing effect, and the air volume flow rate at the scattering outlet has no obvious relationship with the scattering effect. The predicted optimal result is the impeller with 10 blades and 15.13° forward. However, compared with previous research results, this only brings a slight scattering distance improvement. Therefore, increasing the number of blades did not lead to a significant increase in scattering distance. This study will provide effective references for further optimization research of the sand-ejecting fire extinguisher and guide its optimization direction to other parameters of the impeller.     

自动驾驶汽车的高效对抗性场景测试方法研究

在自动驾驶安全性的研究和应用中,测试里程长、暴露危险场景单一的问题使自动驾驶安全性能的提升受到限制。使用对抗性场景进行测试被认为是解决上述问题的重要手段,然而,现有研究采用通用的优化算法作为框架,将大量计算资源浪费在对参数空间的探索过程中,效率低下。在计算成本的约束下,这些算法甚至无法在更复杂的环境中测试出足够多、足够丰富的失效样本。复杂环境中的对抗性场景测试面临三大挑战：信息匮乏;对抗性样本在庞大的参数空间中稀疏分布;搜索过程中探索与利用难以平衡。该文从这三大挑战出发,提出一种高效的对抗性场景测试框架,通过代理模型来获取更多关于参数空间的信息,精选小样本,以打破庞大空间中稀疏事件的制约,对未知区域和对抗性样本附近的目标进行有针对性的搜索和更新,以实现探索和利用的平衡。实验证明,该文提出方法的搜索效率是随机采样的4倍,与通用遗传算法相比,效率提升一倍以上,在有限的仿真测试次数下,生成了更多容易使被测自动驾驶系统失效的对抗性测试用例。特别地,该文提出的方法能够找出许多离群的对抗性样本,揭示出现有算法无法识别的失效模式。此外,该文提出的方法能够快速、全面地定位出被测算法的脆弱场景,为自动驾...     

Uncertainty analysis of structural output with closed-form expression based on surrogate model

Uncertainty analysis (UA) is the process that quantitatively identifies and characterizes the output uncertainty and has a crucial implication in engineering applications. The research of efficient estimation of structural output moments in probability space plays an important part in the UA and has great engineering significance. Given this point, a new UA method based on the Kriging surrogate model related to closed-form expressions for the perception of the estimation of mean and variance is proposed in this paper. The new proposed method is proven effective because of its direct reflection on the prediction uncertainty of the output moments of metamodel to quantify the accuracy level. The estimation can be completed by directly using the redefined closed-form expressions of the model’s output mean and variance to avoid excess post-processing computational costs and errors. Furthermore, a novel framework of adaptive Kriging estimating mean (AKEM) is demonstrated for more efficiently reducing uncertainty in the estimation of output moment. In the adaptive strategy of AKEM, a new learning function based on the closed-form expression is proposed. Based on the closed-form expression which modifies the computational error caused by the metamodeling uncertainty, the proposed learning function enables the updating of metamodel to reduce prediction uncertainty efficiently and realize the decrease in computational costs. Several applications are introduced to prove the effectiveness and efficiency of the AKEM compared with a universal adaptive Kriging method. Through the good performance of AKEM, its potential in engineering applications can be spotted.     

A polynomial chaos efficient global optimization approach for Bayesian optimal experimental design

This paper proposes a global optimization framework to address the high computational cost and non convexity of Optimal Experimental Design (OED) problems. To reduce the computational burden and the presence of noise in the evaluation of the Shannon expected information gain (SEIG), this framework proposes the coupling of Laplace approximation and polynomial chaos expansions (PCE). The advantage of this procedure is that PCE allows large samples to be employed for the SEIG estimation, practically vanishing the noisy introduced by the sampling procedure. Consequently, the resulting optimization problem may be treated as deterministic. Then, an optimization approach based on Kriging surrogates is employed as the optimization engine to search for the global solution with limited computational budget. Four numerical examples are investigated and their results are compared to state-of-the-art stochastic gradient descent algorithms. The proposed approach obtained better results than the stochastic gradient algorithms in all situations, indicating its efficiency and robustness in the solution of OED problems.     

Uncertainty analysis and reliability improvement of planetary roller screw mechanism using active learning Kriging model

The uncertainties in the geometry, material and operation conditions may cause structural failure of the planetary roller screw mechanism (PRSM). The uncertainty analysis model is the key to the reliability assessment of the PRSM, however, the relevant studies have been rarely reported in the past. This paper focuses on establishing a preliminary mathematical model of the PRSM considering uncertain factors. The quasi-Monte Carlo (QMC) method is introduced to improve the solving efficiency of the multidimensional and nonlinear implicit limit state function (LSF). Then, the parameter sensitivities of the uncertain factors to the load distribution and contact characteristics are comprehensively ranked by the design of experiment (DoE). The computational cost for constructing the active learning Kriging (ALK) model of PRSM is decreased by only selecting the most sensitive variables. Moreover, the ALK model and QMC method (ALK-QMC) are combined to explore how the main factors affect the structural reliability of PRSM, which further guides the implementation of multi-objective optimization to improve the reliability by the developed NSGA-II-Downhill algorithm. Finally, the theoretical model and optimization results are verified by the finite element method.     

Operation parameters multi-objective optimization method of large vertical mill based on CFD-DPM

The association mechanism between the main operation parameters and multi-physical fields of the large-scale vertical mill system is unclear, which leads to the difficulty in optimizing operation parameters to improve the performance of large vertical mill systems. To investigate the mechanism of multi-physical field coupling in the operation of the large vertical mill, the numerical simulation method is constructed by coupled CFD-DPM model to calculate the finished product quality, the simulation results were in good agreement with the actual operation results. Based on the Kriging surrogate model, a multi-objective optimization framework for large vertical mills is proposed. Finally, the multi-objective optimization design of LGM large vertical mills is carried out. Combined with CFD-DPM coupling method is developed, design variables and output responses are determined. The Kriging method is used for correlation analysis. The multi-objective optimization function was established. The NSGA-II. optimization algorithm was used to update the surrogate model and obtain the optimal solution, and the optimized operating parameters increased the vertical mill yield by 5.34% and the specific surface area by 9.07%. The maximum relative error between the simulated value and the optimized value is 2.02% through numerical calculation, which verifies the superiority of the optimization method of large vertical mill for performance improvement.     

An efficient method for estimating failure probability bounds under random-interval mixed uncertainties by combining line sampling with adaptive Kriging

For addressing the low efficiency of structural reliability analysis under the random-interval mixed uncertainties (RIMU), this paper establishes the line sampling method (LS) under the RIMU. The proposed LS divides the reliability analysis under RIMU into two stages. The Markov chain simulation is used to efficiently search the design point under RIMU in the first stage, then the upper and lower bounds of failure probability are estimated by LS in the second stage. To improve the computational efficiency of the proposed LS under RIMU, the Kriging model is employed to reduce the model evaluation numbers in the two stages. For efficiently searching the design point, the Kriging model is constructed and adaptively updated in the first stage to accurately recognize the Markov chain candidate state, and then it is sequentially updated by the improved U learning function in the second stage to accurately estimate the failure probability bounds. The proposed LS under RIMU with Kriging model can not only reduce the model evaluation numbers but also decrease the candidate sample pool size for constructing the Kriging model in two stages. The presented examples demonstrate the superior computational efficiency and accuracy of the proposed method by comparison with some existing methods.