Browsing by Author "Wei, Xinpeng"

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    Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface
    (ASME, 2021) Wei, Xinpeng; Zhao, Jianxun; He, Xiaoming; Hu, Zhen; Du, Xiaoping; Han, Daoru; Mechanical and Energy Engineering, School of Engineering and Technology
    This paper presents an adaptive Kriging based method to perform uncertainty quantification (UQ) of the photoelectron sheath and dust levitation on the lunar surface. The objective of this study is to identify the upper and lower bounds of the electric potential and that of dust levitation height, given the intervals of model parameters in the one-dimensional (1D) photoelectron sheath model. To improve the calculation efficiency, we employ the widely used adaptive Kriging method (AKM). A task-oriented learning function and a stopping criterion are developed to train the Kriging model and customize the AKM. Experiment analysis shows that the proposed AKM is both accurate and efficient.
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    Approximation to Multivariate Normal Integral and Its Application in Time-Dependent Reliability Analysis
    (Elsevier, 2021-01) Wei, Xinpeng; Han, Daoru; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and Technology
    It is common to evaluate high-dimensional normal probabilities in many uncertainty-related applications such as system and time-dependent reliability analysis. An accurate method is proposed to evaluate high-dimensional normal probabilities, especially when they reside in tail areas. The normal probability is at first converted into the cumulative distribution function of the extreme value of the involved normal variables. Then the series expansion method is employed to approximate the extreme value with respect to a smaller number of mutually independent standard normal variables. The moment generating function of the extreme value is obtained using the Gauss-Hermite quadrature method. The saddlepoint approximation method is finally used to estimate the cumulative distribution function of the extreme value, thereby the desired normal probability. The proposed method is then applied to time-dependent reliability analysis where a large number of dependent normal variables are involved with the use of the First Order Reliability Method. Examples show that the proposed method is generally more accurate and robust than the widely used randomized quasi Monte Carlo method and equivalent component method.
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    Photoelectron Sheath near the Lunar Surface: Fully Kinetic Modeling and Uncertainty Quantification Analysis
    (American Institute of Aeronautics and Astronautics, 2020-01-05) Zhao, Jianxun; Wei, Xinpeng; Hu, Zhangli; He, Xiaoming; Han, Daoru; Hu, Zhen; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and Technology
    This paper considers plasma charging on the lunar surface with a focus on photoelectron sheath. The plasma species includes ambient solar wind (protons and electrons) and photoelectrons emitted from the illuminated lunar surface. This work is motivated by the high computational cost associated with uncertainty quantification (UQ) analysis of plasma simulations using high-fidelity fully kinetic models. In this paper, we study the photoelectron sheath near the lunar surface with a focus on effects of variables of uncertainty (such as the ambient electron density or photoelectron temperature) on the plasma environment. A fully kinetic 3-D finite-difference (FD) particle-in-cell (PIC) code is utilized to simulate the plasma interaction near the lunar surface and the resulting photoelectron sheath. For the uncertainty quantification analysis, this PIC code is treated as a black box providing high-fidelity quantities of interest, which are also used to construct efficient reduced-order models to perform UQ analysis. A 1-D configuration is first studied to demonstrate the procedure and capability of the UQ analysis. The rest of the paper is organized as follows. Section III presents the analytic and numerical solutions of the 1-D photoelectron sheath. Verification and validation of the FD-PIC code for photoelectron sheath solution is shown. Section IV describes the Kriging model and the uncertainty quantification approach. Section V discusses the UQ analysis of the 1-D photoelectron sheath. The conclusion is given in Section VI.
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    Physics-Based Gaussian Process Method for Predicting Average Product Lifetime in Design Stage
    (ASME, 2021-08) Wei, Xinpeng; Han, Daoru; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and Technology
    The average lifetime or the mean time to failure (MTTF) of a product is an important metric to measure the product reliability. Current methods of evaluating the MTTF are mainly based on statistics or data. They need lifetime testing on a number of products to get the lifetime samples, which are then used to estimate the MTTF. The lifetime testing, however, is expensive in terms of both time and cost. The efficiency is also low because it cannot be effectively incorporated in the early design stage where many physics-based models are available. We propose to predict the MTTF in the design stage by means of a physics-based Gaussian process (GP) method. Since the physics-based models are usually computationally demanding, we face a problem with both big data (on the model input side) and small data (on the model output side). The proposed adaptive supervised training method with the Gaussian process regression can quickly predict the MTTF with a reduced number of physical model calls. The proposed method can enable continually improved design by changing design variables until reliability measures, including the MTTF, are satisfied. The effectiveness of the method is demonstrated by three examples.