skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


  • NSF Public Access
  • Search Results
  • Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface
Title: Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface
Abstract 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.  more » « less
Award ID(s):
1923799
PAR ID:
10247932
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Journal of Verification, Validation and Uncertainty Quantification
Volume:
6
Issue:
1
ISSN:
2377-2158
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, AIAA Scitech 2020 Forum)
    This paper presents a modeling and uncertainty quantification (UQ) study of the photoelectron sheath near the lunar surface. 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 FD-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. 1-D configuration is chosen to present the analytic sheath solution as well as to demonstrate the procedure and capability of the UQ analysis. 
    more » « less
  2. ; ; (, Proceedings of the AAAI Conference on Artificial Intelligence)
    null (Ed.)
    Spatial interpolation is a class of estimation problems where locations with known values are used to estimate values at other locations, with an emphasis on harnessing spatial locality and trends. Traditional kriging methods have strong Gaussian assumptions, and as a result, often fail to capture complexities within the data. Inspired by the recent progress of graph neural networks, we introduce Kriging Convolutional Networks (KCN), a method of combining advantages of Graph Neural Networks (GNN) and kriging. Compared to standard GNNs, KCNs make direct use of neighboring observations when generating predictions. KCNs also contain the kriging method as a specific configuration. Empirically, we show that this model outperforms GNNs and kriging in several applications. 
    more » « less
  3. ; ; (, 3th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP13)
    Risk assessment of power transmission systems against strong winds requires models that can accurately represent the realistic performance of the physical infrastructure. Capturing material nonlinearity, p-delta effects in towers, buckling of lattice elements, joint slippage, and joint failure requires nonlinear models. For this purpose, this study investigates the reliability of transmission line systems by utilizing a nonlinear model of steel lattice towers, generated in OpenSEES platform. This model is capable of considering various geometric and material nonlinearities mentioned earlier. In order to efficiently estimate the probability of failure of transmission lines, the current study adopts an advanced reliability method through Error rate-based Adaptive Kriging (REAK) proposed by the authors. This method is capable of significantly reducing the number of simulations compared to conventional Monte Carlo methods such that reliability analysis can be done within a reasonable time. Results indicate that REAK efficiently estimates the reliability of transmission lines with a maximum of 150 Finite Element simulations for various wind intensities. 
    more » « less
  4. ; ; (, Journal of Vibration and Acoustics)
    Abstract In this study, a two-step experimental procedure is described to determine the electrostatic levitation force in micro-electromechanical system transducers. In these two steps, the microstructure is excited quasi-statically and dynamically and its response is used to derive the electrostatic force. The experimental results are obtained for a 1 mm by 1 mm plate that employs 112 levitation units. The experimentally obtained force is used in a lumped parameter model to find the microstructure response when it is subjected to different dynamical loads. The natural frequency and the damping ratios in the model are identified from the experimental results. The results show that this procedure can be used as a method to extract the electrostatic force as a function of the microstructure’s degrees-of-freedom. The procedure can be easily used for any microstructure with a wide variety of electrode configurations to predict the response of the system to any input excitation. 
    more » « less
  5. ; (, Structural and multidisciplinary optimization)
    The ever-increasing complexity of numerical models and associated computational demands have challenged classical reliability analysis methods. Surrogate model-based reliability analysis techniques, and in particular those using kriging meta-model, have gained considerable attention recently for their ability to achieve high accuracy and computational efficiency. However, existing stopping criteria, which are used to terminate the training of surrogate models, do not directly relate to the error in estimated failure probabilities. This limitation can lead to high computational demands because of unnecessary calls to costly performance functions (e.g., involving finite element models) or potentially inaccurate estimates of failure probability due to premature termination of the training process. Here, we propose the error-based stopping criterion (ESC) to address these limitations. First, it is shown that the total number of wrong sign estimation of the performance function for candidate design samples by kriging, S, follows a Poisson binomial distribution. This finding is subsequently used to estimate the lower and upper bounds of S for a given confidence level for sets of candidate design samples classified by kriging as safe and unsafe. An upper bound of error of the estimated failure probability is subsequently derived according to the probabilistic properties of Poisson binomial distribution. The proposed upper bound is implemented in the kriging-based reliability analysis method as the stopping criterion. The efficiency and robustness of ESC are investigated here using five benchmark reliability analysis problems. Results indicate that the proposed method achieves the set accuracy target and substantially reduces the computational demand, in some cases by over 50%. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email