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1. Adaptive Kriging Method for Uncertainty Quantification of the Photoelectron Sheath and Dust Levitation on the Lunar Surface

   https://doi.org/10.1115/1.4050073  

   Wei, Xinpeng; Zhao, Jianxun; He, Xiaoming; Hu, Zhen; Du, Xiaoping; Han, Daoru (March 2021, Journal of Verification, Validation and Uncertainty Quantification)

   null (Ed.)

   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. 

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2. Kinetic Particle Simulations of Plasma Charging at Lunar Craters Under Severe Conditions

   https://doi.org/10.2514/1.A35622  

   Lund, David; He, Xiaoming; Han, Daoru (July 2023, Journal of Spacecraft and Rockets)

   This paper presents fully kinetic particle simulations of plasma charging at lunar craters with the presence of lunar lander modules using the recently developed Parallel Immersed-Finite-Element Particle-in-Cell (PIFE-PIC) code. The computation model explicitly includes the lunar regolith layer on top of the lunar bedrock, taking into account the regolith layer thickness and permittivity as well as the lunar lander module in the simulation domain, resolving a nontrivial surface terrain or lunar lander configuration. Simulations were carried out to study the lunar surface and lunar lander module charging near craters at the lunar terminator region under mean and severe plasma environments. The lunar module’s position is also investigated to see its effect on the plasma charging relative to the craters. Differential surface charging was clearly resolved by the simulations. For the charging of a lunar lander module made of conducting materials, the results show a near-uniform potential close to that of its surrounding environment and moderate levels of local electric fields. Additionally, the risks associated with charging and discharging increase significantly under a more severe plasma charging environment as shown in the severe plasma environment cases. 

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3. multiUQ: An intrusive uncertainty quantification tool for gas-liquid multiphase flows

   Turnquist, B.; Owkes, M. (July 2018, 14th Triennial International Conference on Liquid Atomization and Spray Systems)

   Assessing the effects of input uncertainty on simulation results for multiphase flows will allow for more robust engineering designs and improved devices. For example, in atomizing jets, surface tension plays a critical role in determining when and how coherent liquid structures break up. Uncertainty in the surface tension coefficient can lead to uncertainty in spray angle, drop size, and velocity distribution. Uncertainty quantification (UQ) determines how input uncertainties affect outputs, and the approach taken can be classified as non-intrusive or intrusive. A classical, non-intrusive approach is the Monte-Carlo scheme, which requires multiple simulation runs using samples from a distribution of inputs. Statistics on output variability are computed from the many simulation outputs. While non-intrusive schemes are straightforward to implement, they can quickly become cost prohibitive, suffer from convergence issues, and have problems with confounding factors, making it difficult to look at uncertainty in multiple variables at once. Alternatively, an intrusive scheme inserts stochastic (uncertain) variables into the governing equations, modifying the mathematics and numerical methods used, but possibly reducing computational cost. In this work, we extend UQ methods developed for single-phase flows to handle gas-liquid multiphase dynamics by developing a stochastic conservative level set approach and a stochastic continuous surface tension method. An oscillating droplet and a 2-D atomizing jet are used to test the method. In these test cases, uncertainty about the surface tension coefficient and initial starting position will be explored, including the impact on breaking/ merging interfaces. 

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4. Uncertainty Quantification of Microstructures: A Perspective on Forward and Inverse Problems for Mechanical Properties of Aerospace Materials

   https://doi.org/10.1002/adem.202401299  

   Billah, Md Maruf; Elleithy, Mohamed; Khan, Waris; Yıldız, Saltuk; Eğer, Zekeriya Ender; Liu, Sheng; Long, Matthew; Acar, Pınar (November 2024, Advanced Engineering Materials)

   In this review, state‐of‐the‐art studies on the uncertainty quantification (UQ) of microstructures in aerospace materials is examined, addressing both forward and inverse problems. Initially, it introduces the types of uncertainties and UQ algorithms. In the review, the forward problem of uncertainty propagation in process–structure and structure–property relationships is then explored. Subsequently, the inverse UQ problem, also known as the design under uncertainty problem, is discussed focusing on structure–process and property–structure linkages. Herein, the review concludes by identifying gaps in the current literature and suggesting key areas for future research, including multiscale topology optimization under uncertainty, implementing physics‐informed neural networks to UQ problems, investigating the effects of uncertainty on extreme mechanical behavior, reliability‐based design, and UQ in additive manufacturing. 

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5. Creating and Evaluating Uncertainty Estimates with Neural Networks for Environmental-Science Applications

   https://doi.org/10.1175/AIES-D-22-0061.1  

   Haynes, Katherine; Lagerquist, Ryan; McGraw, Marie; Musgrave, Kate; Ebert-Uphoff, Imme (January 2023, Artificial Intelligence for the Earth Systems)

   Abstract Neural networks (NN) have become an important tool for prediction tasks – both regression and classification – in environmental science. Since many environmental-science problems involve life-or-death decisions and policy-making, it is crucial to provide not only predictions but also an estimate of the uncertainty in the predictions. Until recently, very few tools were available to provide uncertainty quantification (UQ) for NN predictions. However, in recent years the computer-science field has developed numerous UQ approaches, and several research groups are exploring how to apply these approaches in environmental science. We provide an accessible introduction to six of these UQ approaches, then focus on tools for the next step, namely to answer the question: Once we obtain an uncertainty estimate (using any approach), how do we know whether it is good or bad? To answer this question, we highlight four evaluation graphics and eight evaluation scores that are well suited for evaluating and comparing uncertainty estimates (NN-based or otherwise) for environmental-science applications. We demonstrate the UQ approaches and UQ-evaluation methods for two real-world problems: (1) estimating vertical profiles of atmospheric dewpoint (a regression task) and (2) predicting convection over Taiwan based on Himawari-8 satellite imagery (a classification task). We also provide Jupyter notebooks with Python code for implementing the UQ approaches and UQ-evaluation methods discussed herein. This article provides the environmental-science community with the knowledge and tools to start incorporating the large number of emerging UQ methods into their research. 

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