Article
Energy & Fuels
Nicolas Leclaire, John Darrell Bess
Summary: This study tested two major methods for dealing with the assessment of rod positioning uncertainty, showing that different methods can have significant impacts on uncertainty propagation and potential biases.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Engineering, Marine
Longquan Sun, Wenpeng Li, Guihui Ma, Yingyu Chen, Ming Fang, Wangkai Zhang, Xiongliang Yao
Summary: This paper investigates the uncertainty of ventilated cavities under stochastic conditions, showing that cavity length and re-entrant pressure at the cavity tail are sensitive to stochastic conditions, while the structure and strength of the vortex in the air phase region are not sensitive. The results also indicate that the covering area of the ventilated cavity provides good pressure-equalizing robustness under stochastic conditions.
Article
Engineering, Multidisciplinary
Xiukai Yuan, Jian Gu, Shaolong Liu
Summary: This study proposed contribution indexes to measure the sensitivity of failure probability estimate with regards to sample, and derived and analyzed them for four simulation methods. The main differences between these methods lie in the contribution indexes of the safety samples, which are key factors to the efficiency of the methods. Numerical examples were used to validate the findings.
CMES-COMPUTER MODELING IN ENGINEERING & SCIENCES
(2021)
Article
Thermodynamics
Wenyi Du, Juan Ma, Wanjun Yin
Summary: This paper firstly uses Monte Carlo simulation to study the disorderly charging behavior of electric vehicles (EVs), and presents an improved particle swarm optimization (PSO) algorithm to model the orderly charging strategy. By adjusting the inertia weight index and learning factor, the problems of poor local optimization ability and premature convergence of the original PSO are alleviated. Experimental results demonstrate that the proposed orderly charging strategy can significantly reduce charging costs and peak-valley differences.
Article
Computer Science, Interdisciplinary Applications
Tirthankar Roy, Hoshin Gupta
Summary: Model-based simulations often use prediction interval estimates, which may underestimate the width of the intervals; adjusting the interval width can lead to better estimation of prediction intervals; the method is applicable to different probability density functions and particularly useful when large samples are not available.
ENVIRONMENTAL MODELLING & SOFTWARE
(2021)
Review
Multidisciplinary Sciences
Chong Wang, Haoran Fan, Xin Qiang
Summary: This paper provides an overview of currently available UMDO technologies, with a special focus on relevant intelligent optimization strategies. It introduces the complexity and uncertainty issues in aerospace system design and proposes research directions to address these challenges.
Article
Environmental Sciences
G. Q. Yang, M. Li, P. Guo
Summary: This paper established an optimal water allocation model for the Shijin irrigation district under uncertainty, considering uncertainties of key parameters. The analysis showed significant changes in water allocation amounts for winter wheat and maize in 2016, with winter wheat receiving a considerably larger amount of irrigation water.
JOURNAL OF ENVIRONMENTAL INFORMATICS
(2022)
Article
Environmental Sciences
G. Q. Yang, M. Li, P. Guo
Summary: This paper established an agricultural water optimal allocation model under uncertainty and obtained agricultural water allocation schemes using Monte Carlo simulation technique. The optimized results showed the relationship between system benefits and water amounts, with significant impact of crop purchase prices on water allocation.
JOURNAL OF ENVIRONMENTAL INFORMATICS
(2022)
Article
Engineering, Geological
Bak Kong Low, Chia Weng Boon
Summary: This study explores the coupling of the first-order reliability method (FORM) and Monte Carlo simulations (MCS) in the probability-based design of reinforced rock slopes, and proposes the FORM-MCS-FORM design method for cases with multiple failure modes. For cases with a dominant single failure mode, importance sampling or the fast second-order reliability method (SORM) can be used instead of MCS. Additionally, MCS enhanced with FORM is essential for reinforced blocks with multiple sliding modes.
ROCK MECHANICS AND ROCK ENGINEERING
(2023)
Article
Nuclear Science & Technology
Nguyen Huu Tiep, Kyung-Doo Kim, Jaeseok Heo, Chi-Woong Choi, Hae-Yong Jeong
Summary: This study developed a new data assimilation framework, STARU, to improve the prediction accuracy of complex systems. Compared to existing data assimilation toolkit, STARU performs better in dealing with problems with many parameters and highly non-linear systems, while also reducing computation time. Experimental results show that STARU effectively enhances the data assimilation results for the reflood tests, achieving better improvements and more stable convergence of the system states.
NUCLEAR ENGINEERING AND DESIGN
(2022)
Article
Chemistry, Physical
Feng Zhang, Xinhe Wang, Mingying Wu, Xinting Hou, Cheng Han, Zhongbing Liu
Summary: This study demonstrated the deterministic and robust optimization design of photovoltaic cells by considering uncertain factors, reducing the influence of uncertainties, and improving output reliability.
JOURNAL OF POWER SOURCES
(2022)
Article
Engineering, Mechanical
Andriy Prots, Matthias Voigt, Ronald Mailach
Summary: This paper introduces a new sampling method called Latinized Particle Sampling (LPS), which distributes samples uniformly in the sample space. By treating the samples as charged particles and repelling each other, a force equilibrium is achieved through iterative process, resulting in desired marginal distributions and target correlation control. Compared to regular Latin Hypercube sampling (LHS), LPS improves the quality of surrogate models and can be created much faster.
PROBABILISTIC ENGINEERING MECHANICS
(2023)
Article
Engineering, Multidisciplinary
L. C. Diaz-Perez, M. Torralba, L. Muro, J. A. Albajez, J. A. Yaguee-Fabra
Summary: The objective of precision systems design is to obtain machines with high and predictable work-zone accuracies. Error correction and compensation are used in already functional systems to achieve this goal. This study proposes an uncertainty budget methodology to obtain the measuring uncertainty of a nano-positioning platform, NanoPla, after error compensation. The authors use the Monte Carlo method to calculate the final measuring uncertainty considering all possible cases along the NanoPla working range, and propose solutions to minimize it.
Article
Computer Science, Artificial Intelligence
Hanan Hiba, Shahryar Rahnamayan, Azam Asilian Bidgoli, Amin Ibrahim, Rasa Khosroshahli
Summary: Metaheuristic algorithms are well-established approaches for solving complex real-world optimization problems, and the concept of center-based sampling has been introduced as a way to improve the optimization process. This study comprehensively investigates the impact of center-based sampling on solving optimization problems at different levels of detail. The experimental results confirm the crucial role of center-based sampling in improving the convergence rate of optimization algorithms for high-dimensional problems.
SWARM AND EVOLUTIONARY COMPUTATION
(2022)
Article
Management
Eunji Lim, Peter W. Glynn
Summary: This paper discusses the use of simulation in computing predictors when real-world observations are collected. The challenge is that the simulation's state description often includes unobserved information from the real system. The authors propose an estimation methodology that involves launching multiple simulations from states closely aligned with the most recent real-world observation.
OPERATIONS RESEARCH
(2022)