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
Optics
Jianhui Zhao, Bing Pan
Summary: Three-dimensional digital image correlation (3D-DIC) is a leading optical measurement technique for measuring full-field shape, displacement, and deformation of solid materials and structures. However, the uncertainty quantification (UQ) of 3D-DIC measurements is less advanced and less widely practiced. This work proposes a Monte Carlo-based method to quantify the uncertainty of 3D-DIC displacement measurements. The proposed method can be integrated with existing 3D-DIC software to quantify the metrological performance of 3D-DIC measurements and therefore better interpret the measurement results.
OPTICS AND LASERS IN ENGINEERING
(2023)
Article
Engineering, Aerospace
Qian Zhang, Shenren Xu, Xianjun Yu, Jiaxin Liu, Dingxi Wang, Xiuquan Huang
Summary: This study proposes an improved method to address the accuracy issue of MC-adj-linear without incurring high costs. The method is applied to the study of aerodynamic performance and demonstrates great potential for fast uncertainty quantification.
CHINESE JOURNAL OF AERONAUTICS
(2022)
Article
Engineering, Electrical & Electronic
Xiaojie Zhu, Luca Di Rienzo, Xikui Ma, Lorenzo Codecasa
Summary: The multilevel Monte Carlo method improves the accuracy of uncertainty quantification in FDTD methods, reduces computational costs, and outperforms polynomial chaos FDTD and stochastic FDTD in terms of accuracy.
IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS
(2022)
Article
Engineering, Aerospace
Shenren Xu, Qian Zhang, Dingxi Wang, Xiuquan Huang
Summary: This study investigates the suitability of the MC-adj-nonlinear method to assess performance discrepancies of a transonic compressor blade section. The method demonstrates impressive generalization capabilities and offers a great balance between precision and efficiency. Geometric deviations have a significant impact on aerodynamic performance, especially at high speeds.
Article
Engineering, Civil
Massimiliano Schiavo
Summary: In this study, the quantification of subsurface discharges in alluvial aquifers in Northern Italy is conducted using a Monte Carlo framework. The results show that geological uncertainty has a greater impact on the quantification of subsurface discharges than conditioning data.
JOURNAL OF HYDROLOGY
(2023)
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
Computer Science, Information Systems
Emanuele Ledda, Giorgio Fumera, Fabio Roli
Summary: Among Bayesian methods, Monte Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that proposed activating the dropout layers only during inference for evaluating epistemic uncertainty. This approach, which we call dropout injection, provides clear benefits over its traditional counterpart (which we call embedded dropout) since it allows one to obtain a post hoc uncertainty measure for any existing network previously trained without dropout, avoiding an additional, time-consuming training process.
INFORMATION SCIENCES
(2023)
Article
Nuclear Science & Technology
Abdulla Alhajri, Vladimir Sobes, Kord Smith, Benoit Forget
Summary: Managing uncertainties in fundamental parameters is crucial in the safety-oriented nuclear engineering world. Large uncertainties in neutron cross sections of materials can lead to significant uncertainties in predicted behaviors. The only solution to addressing safety concerns related to large uncertainties is to reduce the uncertainties in input neutron cross sections, rather than relying on over designing which can increase the cost of nuclear reactors.
ANNALS OF NUCLEAR ENERGY
(2021)
Article
Engineering, Geological
Liang Han, Lin Wang, Wengang Zhang, Zhixiong Chen
Summary: This study presented a database of UCS from four sites in the Bukit Timah Granite formation in Singapore, and used Bayesian method and MCMC algorithm to quantitatively evaluate the uncertainties of statistical characteristics of UCS. The results showed significant statistical uncertainties of the three statistical characteristics of BTG rocks, which somewhat rely on the selection of basic parameters and autocorrelation function classes.
GEORISK-ASSESSMENT AND MANAGEMENT OF RISK FOR ENGINEERED SYSTEMS AND GEOHAZARDS
(2022)
Article
Engineering, Chemical
Yota Yamamoto, Tomoyuki Yajima, Yoshiaki Kawajiri
Summary: A sequential Monte Carlo (SMC) parameter estimation method was developed for chromatographic processes to rigorously estimate parameter uncertainty, showing higher efficiency compared to existing methods and reducing time and effort for experimental data analysis.
CHEMICAL ENGINEERING RESEARCH & DESIGN
(2021)
Article
Construction & Building Technology
Qiangqiang Sun, Daniel Dias
Summary: Tunnel damages are increasing worldwide in major earthquakes, and this research introduces stochastic dynamic time-history analysis to quantify the variability of seismic-induced tunnel deformations in a probabilistic framework. The results indicate that parameter uncertainty has a significant impact on tunnel seismic deformations, including an increase in standard deviation with increased motion intensity and the effect of distribution types on the results.
TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY
(2022)
Article
Environmental Sciences
G. Aitken, L. Beevers, M. A. Christie
Summary: Flood events are the most common natural disaster, and climate change is expected to increase their frequency and intensity. Probabilistic flood modeling can help reduce the impact of floods, but its applicability is limited by computational cost and model resolution. This paper examines improvements to traditional Monte Carlo methods using Latin hypercube sampling and Multi-level Monte Carlo to reduce computational cost and improve output resolution.
WATER RESOURCES RESEARCH
(2022)
Article
Computer Science, Artificial Intelligence
Pieter Van Molle, Tim Verbelen, Bert Vankeirsbilck, Jonas De Vylder, Bart Diricx, Tom Kimpe, Pieter Simoens, Bart Dhoedt
Summary: The paper highlights the limitations of conventional neural networks in capturing uncertainty and introduces Bayesian techniques such as Monte Carlo dropout. The authors propose a novel method based on the overlap of output distributions of different classes to better approximate inter-class output confusion. They demonstrate the advantages of their approach using benchmark datasets and skin lesion classification.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Mathematics, Interdisciplinary Applications
Chensen Ding, Kumar K. Tamma, Haojie Lian, Yanjun Ding, Timothy J. Dodwell, Stephane P. A. Bordas
Summary: This paper introduces a novel method to quantify the impact of load uncertainty on structural response. The study shows that spatially uncorrelated load uncertainty significantly decreases the standard deviation of the output, but the expected values remain consistent.
COMPUTATIONAL MECHANICS
(2021)
Article
Acoustics
Gabriel Yuji Garoli, Helio Fiori de Castro
JOURNAL OF SOUND AND VIBRATION
(2019)
Article
Engineering, Mechanical
Diogo Stuani Alves, Gregory Bregion Daniel, Helio Fiori de Castro, Tiago Henrique Machado, Katia Lucchesi Cavalca, Ozhan Gecgel, Joao Paulo Dias, Stephen Ekwaro-Osire
MECHANISM AND MACHINE THEORY
(2020)
Article
Engineering, Multidisciplinary
Gabriel Yuji Garoli, Diogo Stuani Alves, Tiago Henrique Machado, Katia Lucchesi Cavalca, Helio Fiori de Castro
Summary: Fault identification is crucial in the field of rotating machines, enabling better maintenance of expensive equipment. Common faults like unbalance and bearing wear can be identified through deterministic or stochastic methods. The Bayesian inference with polynomial chaos showed promising results for reliable identification of fault parameters.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2021)
Article
Engineering, Mechanical
Gabriel Y. Garoli, Rafael Pilotto, Rainer Nordmann, Helio F. de Castro
Summary: This study focuses on the application and parameter identification of active magnetic bearings (AMB) in rotating machines. By using Bayesian inference and generalized polynomial chaos expansion, the solution process is accelerated and robustness evaluation and sensitivity analysis are conducted.
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2021)
Article
Engineering, Mechanical
Lais Bittencourt Visnadi, Roberto Rema Gaudeoso Filho, Helio Fiori de Castro
Summary: Fault diagnosis is a crucial process in modern industry management. This study investigates the dynamic effect of gear tooth crack on rotor response caused by bending stress and analyzes the impact of different parameters on the system response through experiments.
ENGINEERING FAILURE ANALYSIS
(2022)
Article
Engineering, Multidisciplinary
Lucas Nogueira Garpelli, Diogo Stuani Alves, Katia Lucchesi Cavalca, Helio Fiori de Castro
Summary: Rotary systems are crucial for industrial production, but they are prone to unbalance faults. This paper proposes a method using Physics-Guided Neural Networks to identify and prevent rotor unbalance faults. The results show that the Physics-Guided Neural Networks have smaller errors and better performance compared to standard Artificial Neural Networks.
STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL
(2023)
Article
Engineering, Mechanical
Ozhan Gecgel, Joao Paulo Dias, Stephen Ekwaro-Osire, Diogo Stuani Alves, Tiago Henrique Machado, Gregory Bregion Daniel, Helio Fiori de Castro, Katia Lucchesi Cavalca
Summary: A framework using a deep learning algorithm to classify wear faults in hydrodynamic journal bearings was proposed, showing promising results for early diagnosis of wear faults in rotating machinery.
JOURNAL OF TRIBOLOGY-TRANSACTIONS OF THE ASME
(2021)
Article
Engineering, Mechanical
Gabriel Yuji Garoli, Helio Fiori de Castro
JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING
(2020)
Proceedings Paper
Engineering, Mechanical
Lais Bittencourt Visnadi, Gabriel Yuji Garoli, Helio Fiori de Castro
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON ROTOR DYNAMICS - IFTOMM, VOL. 4
(2019)
Proceedings Paper
Engineering, Mechanical
Andre Morais Ferreira, Helio Fiori de Castro
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON ROTOR DYNAMICS - IFTOMM, VOL. 4
(2019)
Proceedings Paper
Engineering, Mechanical
Gabriel Yuji Garoli, Natalia Cezaro Tyminski, Helio Fiori de Castro
PROCEEDINGS OF THE 10TH INTERNATIONAL CONFERENCE ON ROTOR DYNAMICS - IFTOMM, VOL. 4
(2019)
Article
Acoustics
F. W. S. Tuckmantel, H. F. Castro, K. L. Cavalca
JOURNAL OF VIBRATION AND ACOUSTICS-TRANSACTIONS OF THE ASME
(2020)
Article
Engineering, Mechanical
Antonella Castellano, Pietro Stano, Umberto Montanaro, Marco Cammalleri, Aldo Sorniotti
Summary: This paper proposes a new control strategy for hybrid electric vehicles, called Model Predictive Control (MPC), and considers the losses in transmission gears. Through a case study on Chevrolet Volt, the results show that the simplified internal model has a minor impact on fuel consumption performance.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Rui Peng, Gregory S. Chirikjian
Summary: This article introduces a method of designing morphable thick-panel origami structures using reconfigurable linkages, which improves the potential of origami techniques for different tasks and solves the limitations of one-DOF and multiple-DOF folding structures.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Gaohan Zhu, Weizhong Guo, Yinghui Li, Youcheng Han
Summary: Comprehensive and accurate performance evaluation is crucial for profile synthesis and analysis of higher pair mechanisms. This paper proposes evaluation indices and methods for the transmission performance of planar higher pair mechanisms from different perspectives. It subdivides the transmission performance into element-based performance and joint-based performance and develops novel indices specific to higher pair mechanisms. A graphical mapping method based on element-based performance is also proposed for intuitive analysis. Practical examples validate the effectiveness of the proposed indices and methods for evaluating the performance of higher pair mechanisms.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Ke Wu, Gang Zheng, Guimin Chen, Shorya Awtar
Summary: Researchers proposed a new modeling method, namely Body-frame Beam Constraint Model (BBCM), to predict and optimize the design of high-precision compliant mechanisms (CMs).
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Youcheng Han, Weizhong Guo, Changjie Zhao, Ziyue Li, Ze Fu, Yinghui Li
Summary: This study proposes a structural synthesis methodology that considers motion, force, and energy characteristics simultaneously to design efficient mechanisms.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Cristian Enrico Capalbo, Daniel De Gregoriis, Tommaso Tamarozzi, Giuseppe Carbone, Domenico Mundo
Summary: This study proposes a novel flexible multibody formulation that enables efficient updating of models while maintaining small size and high accuracy. Numerical validation demonstrates its wide applicability across various materials and mechanisms, showing promising results in terms of accuracy.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Weihao Zhao, Junbei Liao, Wei Qian, Haoyong Yu, Zhao Guo
Summary: This paper presents a newly designed compliant actuator using a tensile springs array to address the challenges in achieving linear and consistent elastic properties, low friction, minor hysteresis, and good compliance in series elastic actuators (SEA). The unique geometry of the spring array enables the SEA to have consistent rotary stiffness with minimal friction and hysteresis. The device's performance is evaluated using PID and sliding mode control, demonstrating its constant low rotary stiffness and torque tracking bandwidth, making it suitable for human-robot interaction requirements.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Mohui Jin, Yukang Luo, Xing Xu, Bowei Xie, Weisheng Wang, Zewei Li, Zhou Yang
Summary: This paper presents a method for evaluating the contact interaction between compliant mechanisms and external objects. By establishing a numerical model and introducing contact springs to describe the contact forces, the deformation and normal contact force/stress can be accurately calculated. The static equilibrium configuration and contact force/stress can be obtained by minimizing the total potential energy function of the system.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Alejandro G. Gallardo, Martin A. Pucheta
Summary: This paper presents a method for the synthesis of parallel flexure systems using Screw Theory and Linear Algebra. The method is validated through three case studies and offers a simple and precise design with decoupled actuators.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Xiao Wang, Chenglin Liu, Haoxiang Sun, Hanwen Song
Summary: This paper presents a new decomposition mode for robot-world calibration, which decomposes the Ad(SE(3)) equation using Chasles' motion. A two-step method based on point set matching is proposed. The superiority of this method is verified through simulations and experiments.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Yanlin Chen, Xianmin Zhang, Yanjiang Huang, Yanbin Wu, Jun Ota
Summary: This study establishes an error model for a 3-RRR+UR spherical parallel mechanism and analyzes the sensitivity of error parameters. A design structure is proposed to reduce input errors based on the analysis. Experimental results show that the multiloop circuit incremental method provides more accurate results.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Vu Linh Nguyen, Chin-Hsing Kuo, Po Ting Lin
Summary: This paper presents a method for analyzing the performance of gravity-balanced serial robotic manipulators under dynamic loads and uses a three-degree-of-freedom planar serial manipulator as a case study. The significance of this method is demonstrated by evaluating the impact of dynamic loads on gravity-balanced performance and proposing a step-by-step design procedure to improve it.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Shifeng Rong, Jiange Zhang, Xing Zhang, Keliang Li, Kaibin Rong, Zhenyu Zhou, Han Ding
Summary: This article proposes a data-driven dry cutting tool collaborative optimization model to improve the economic and environmental attributes of facehobbing hypoid gears. An innovative ease-off tooth contact analysis method is introduced to establish accurate relations between ease-off flank and loaded contact performance evaluations. The proposed model significantly improves sustainability in terms of economic and environmental assessments.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Kemal Eren, Soley Ersoy, Ettore Pennestri
Summary: This research investigates the instantaneous kinematics of the terminal link of a planar two-link open chain using the complex-number technique and higher-order instantaneous invariants.
MECHANISM AND MACHINE THEORY
(2024)
Article
Engineering, Mechanical
Bo Han, Zhantu Yuan, Jiachuan Zhang, Yundou Xu, Jiantao Yao, Yongsheng Zhao
Summary: This paper proposes novel deployable mechanism units with self-limiting position function, and constructs ring truss deployable mechanisms. The degrees of freedom (DOF) of deployable units are analyzed and it is proved that the constructed ring truss deployable mechanisms have only one DOF. The dynamic model of the deployable mechanism unit with passive actuation is established and verified by simulation. The deployable mechanism units proposed in this paper have the advantages of good scalability and stability, and have broad application prospects.
MECHANISM AND MACHINE THEORY
(2024)
