Article
Engineering, Petroleum
F. Cala, E. Nunez, N. Bahamon, J. A. Fuentes
Summary: A comparison was made between uncertainty assessments using the GUM framework and the Monte Carlo method, confirming the adequacy of the GUM method for liquid hydrocarbon measurements with a vertical fixed-top tank and a positive displacement meter. The maximum differences between GUM and MCM uncertainties were found to be within acceptable tolerance levels for both examples.
Article
Pathology
Yong Kwan Lim, Oh Joo Kweon, Mi-Kyung Lee, Hye Ryoun Kim
Summary: This study quantified measurement uncertainty for factor activities using Monte Carlo simulation, finding that MCS results were interchangeable with actual sample results. Therefore, the MCS procedure is well suited for quantifying uncertainties in factor assays over the entire measurement range.
AMERICAN JOURNAL OF CLINICAL PATHOLOGY
(2021)
Article
Geosciences, Multidisciplinary
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, Thorsten Wagener
Summary: The CREDIBLE Uncertainty Estimation (CURE) toolbox is an open-source MATLAB(TM) toolbox that provides a range of Monte Carlo methods for uncertainty estimation in environmental simulation models. It aims to help scientists and practitioners without expertise in uncertainty estimation by providing examples of modelling applications and workflow scripts.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2023)
Article
Computer Science, Interdisciplinary Applications
Jan I. C. Vermaak, Jim E. Morel
Summary: The residual Monte Carlo (RMC) method is a Monte Carlo approach for solving linear equations that directly computes the error associated with an approximate solution, making it more efficient than the standard Monte Carlo (SMC) method.
JOURNAL OF COMPUTATIONAL PHYSICS
(2022)
Article
Engineering, Marine
Oyvind Oksnes Dalheim, Sverre Steen
Summary: The ship speed through water (STW) is crucial variable for ship performance evaluation, and can be estimated using speed logs or in-service measurements on the propeller shaft. The propeller pitch angle is found to have a significant contribution to the total uncertainty in STW. Including thrust measurements decreases the uncertainty in STW by 34%, and the uncertainty is not significantly affected by the presence of waves.
Article
Automation & Control Systems
Girija Moona, Vinod Kumar, Mukesh Jewariya, Harish Kumar, Rina Sharma
Summary: Precise dimensional measurements in manufacturing industries are largely influenced by coordinate metrology. An experimental investigation was conducted to verify an articulated arm coordinate measuring machine using a one-dimensional standard artefact. Monte Carlo Simulation method was used for uncertainty estimation and compared with ISO GUM outcomes, showing reasonable concordance.
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY
(2022)
Article
Computer Science, Artificial Intelligence
Rammohan Mallipeddi, Iman Gholaminezhad, Mohammad S. Saeedi, Hirad Assimi, Ali Jamali
Summary: This paper introduces a multi-objective evolutionary algorithm combined with Monte Carlo simulations for optimal stochastic design of robust controllers for uncertain time-delay systems. Genetic programming is utilized to evolve robust controllers and a new adaptive operator is employed to balance exploration and exploitation.
Review
Computer Science, Interdisciplinary Applications
Samira Mohammadi, Selen Cremaschi
Summary: This study computationally evaluates the performance of commonly used uncertainty propagation methods in estimating the statistical moments of model outputs with uncertain inputs, revealing that numerical integration methods deteriorate with an increase in uncertain inputs, while Monte-Carlo simulation methods converge to the true values of moments with minimal model evaluations.
COMPUTERS & CHEMICAL ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Alexandre S. Avaro, Juan G. Santiago
Summary: This article presents a quantification of the uncertainty in the experimental determination of kinetic rate parameters for enzymatic reactions. The authors examine several sources of uncertainty and bias and compute typical uncertainties of kcat, KM, and catalytic efficiency. The extraction of these parameters for CRISPR-Cas systems is analyzed as a salient example. Reports of enzymatic kinetic rates for CRISPR diagnostics have been highly unreliable and inconsistent.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2022)
Article
Engineering, Geological
Juan C. Viviescas, Alvaro J. Mattos, Juan P. Osorio
Summary: This study quantifies the uncertainty of different Bearing capacity (BC) methods for shallow foundations supported by anthropic sandy soil. The choice of the most appropriate friction angle correlation may lead to BC overestimations, especially in more sophisticated methods like Finite elements (FE) and Random Finite elements (RFEM). The bias in FE and RFEM methods is linked to the quality of laboratory results and correlation length estimation.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2021)
Article
Geosciences, Multidisciplinary
Alexander Schaaf, Miguel de la Varga, Florian Wellmann, Clare E. Bond
Summary: This paper introduces a method to incorporate geological information into probabilistic geomodeling using the open-source software GemPy. By checking simulated geomodel realizations against topology information without specifying a likelihood function, the method demonstrates the feasibility of constraining and improving probabilistic geomodel ensembles with reduced uncertainty.
GEOSCIENTIFIC MODEL DEVELOPMENT
(2021)
Article
Engineering, Industrial
Robert Millar, Hui Li, Jinglai Li
Summary: In many engineering systems, the performance or reliability is characterized by a scalar variable. The distribution of this variable is important for uncertainty quantification in various applications. Standard Monte Carlo simulations are often used but struggle to efficiently estimate the tail of the distribution. The Multicanonical Monte Carlo method provides an adaptive importance sampling scheme, where samples are drawn from a nonstandard importance sampling distribution using Markov chain Monte Carlo (MCMC). However, MCMC is inherently serial and difficult to parallelize. In this paper, we propose a new approach that uses the Sequential Monte Carlo sampler for parallel implementation and demonstrate its competitive performance with mathematical and practical examples.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Anupam Prasad Vedurmudi, Katharina Janzen, Markus Nagler, Sascha Eichstaedt
Summary: Accurate measurements in dimensional metrology require strict controls on temperature variations in the measurement room. This study uses Kriging to interpolate room temperatures from a limited number of sensors and demonstrates a novel method to propagate sensor uncertainties using a Monte Carlo simulation. The study also investigates the influence of a movable heating element in the room.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2023)
Article
Energy & Fuels
Bhargav Appasani, Amitkumar V. Jha, Kunjabihari Swain, Murthy Cherukuri, Dusmanta Kumar Mohanta
Summary: With the evolution of power grid into a smart grid, continuous real-time monitoring is necessary for safe operation. This study proposes a quantitative metric for estimating the resilience of smart grids and uses a genetic algorithm to optimize the placement of measurement units, aiming to improve resilience. A case study on the practical power grid in West Bengal, India, is conducted.
FRONTIERS IN ENERGY RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Dinesh D. Dhadekar, Prithvi D. Sanghani, K. K. Mangrulkar, S. E. Talole
Summary: This paper investigates a robust position and attitude tracking control problem of a quadrotor under system nonlinearities, input coupling, aerodynamic uncertainties, and external wind disturbances. A robustified nonlinear dynamic inversion (NDI) control scheme based on Uncertainty and Disturbance Estimator (UDE) technique is proposed, and its effectiveness is confirmed through simulations and experimental validation.
JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS
(2021)