Article
Metallurgy & Metallurgical Engineering
Marks Legkovskis, Peter J. Thomas, Michael Auinger
Summary: Uncertainty quantification is crucial in steel reheating simulations due to input uncertainties in defining surface properties and furnace conditions. The study uses polynomial chaos expansion to reduce computational effort and presents a comprehensive uncertainty quantification analysis of a walking-beam reheat furnace. The analysis reveals the significant influence of parameters related to emissivity and oxide scale growth on slab temperature and identifies the transition in importance of oxide scale growth inputs.
STEEL RESEARCH INTERNATIONAL
(2023)
Article
Engineering, Marine
Ming Chen, Xinhu Zhang, Kechun Shen, Guang Pan
Summary: This study investigates the high-dimensional uncertainty quantification of critical buckling pressure for a composite cylindrical shell with geometric and material uncertainties using sparse polynomial chaos expansion (PCE). The results show that the uncertainty of the longitudinal modulus has a massive influence on the critical buckling pressure, while the uncertainties of other parameters have a weak influence.
JOURNAL OF MARINE SCIENCE AND ENGINEERING
(2022)
Article
Engineering, Civil
Z. P. Xu, Y. P. Li, G. H. Huang, Z. Y. Shen
Summary: In this study, a PCE-ANOVA-RF method is developed to analyze the effects of multiple uncertain parameters in the SWAT model and generate probabilistic forecasts of daily streamflow. The proposed method not only reveals the impact of parameter uncertainty and saves computation time, but also expands PCE's ability to predict future streamflow processes. The feasibility and applicability of the method are verified in the Amu Darya River Basin in Central Asia.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Multidisciplinary
Arash Mohammadi, Koji Shimoyama, Mohamad Sadeq Karimi, Mehrdad Raisee
Summary: An efficient surrogate model based on POD and compressed sensing is developed for affordable representation of high-dimensional stochastic fields, showing potential in engineering applications.
APPLIED MATHEMATICAL MODELLING
(2021)
Article
Mathematics, Applied
Xiang Sun, Jung-Il Choi
Summary: The proposed method utilizes POD and PCE to model spacetime-dependent parameterized problems, effectively estimating low-order moments and accuracy loss under uncorrelated or correlated input parameters.
COMPUTERS & MATHEMATICS WITH APPLICATIONS
(2021)
Article
Mathematics, Applied
Y. Wei, F. Vazeille, Q. Serra, E. Florentin
Summary: PCE is a powerful metamodeling technique, but requires exponentially increasing training samples with problem dimensionality. PGD has emerged as a popular solution with linear complexity growth based on separate representations. This work introduces a hybrid technique called PGD-PCE, utilizing orthonormal polynomial functions, demonstrating good accuracy and computational efficiency in handling large problems.
FINITE ELEMENTS IN ANALYSIS AND DESIGN
(2022)
Article
Engineering, Industrial
Wen Yao, Xiaohu Zheng, Jun Zhang, Ning Wang, Guijian Tang
Summary: This paper proposes an adaptive arbitrary polynomial chaos (aPC) method and combines it with a deep neural network (DNN) to propose a semi-supervised deep adaptive arbitrary polynomial chaos expansion (Deep aPCE) method. The Deep aPCE method reduces the training data cost by using a small amount of labeled data and abundant unlabeled data, and improves the accuracy of uncertainty quantification by dynamically fine-tuning the adaptive expansion coefficients using DNN. Additionally, the Deep aPCE method can construct accurate surrogate models of high dimensional stochastic systems.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Multidisciplinary
Zhiheng Wang, Roger Ghanem
Summary: This paper presents a method to evaluate the predictive accuracy of stochastic models in the presence of model error and sparse data. Model error is introduced as uncertainty in the coefficients of polynomial chaos expansions. The coefficients are treated as random variables and their influence on the output quantities of interest is evaluated using an extended polynomial chaos expansion. A Bayesian data assimilation scheme is used to update these expansions, treating the resulting nested chaos expansion as a hierarchical probabilistic model. Stochastic models of quantities of interest are then constructed and efficiently evaluated using the Metropolis-Hastings Markov chain Monte Carlo procedure.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2023)
Article
Engineering, Industrial
Mishal Thapa, Samy Missoum
Summary: This paper presents a framework for uncertainty quantification (UQ) and global sensitivity analysis (GSA) of composite wind turbine blades using polynomial chaos expansion (PCE) with l(1)-minimization. The framework is capable of handling a large number of random parameters and can assess the relative importance of these parameters using Sobol Indices. It also allows for arbitrary distributions of random inputs and spatial variations of material and geometric properties. The presented framework is applied to three composite wind turbine blade problems, and results are compared to Monte Carlo simulations.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2022)
Article
Engineering, Aerospace
Loic Brevault, Mathieu Balesdent
Summary: The early design phase of launch vehicles often involves low fidelity models with high levels of modeling uncertainties. These uncertainties need to be propagated throughout the design process to ensure robustness, which can be computationally costly due to trajectory optimization and uncertainty quantification.
Article
Engineering, Civil
Vinh Ngoc Tran, Jongho Kim
Summary: This study introduces the strengths of polynomial chaos-kriging (PCK), a new surrogate model that combines polynomial chaos extension (PCE) and Gaussian process with kriging variance. The results show that PCK outperforms PCE and ordinary kriging (OK) in mimicking predictive and sensitive behaviors of the original model with a smaller-sized training dataset. Additionally, PCK accurately predicts hydrographs and flood peaks for extreme events that differ significantly from the training set.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Vinh Ngoc Tran, Jongho Kim
Summary: This study demonstrates that the SPCE approach outperforms the FPCE approach in terms of accuracy and efficiency in building surrogate models for hydrological systems. The SPCE method can efficiently capture streamflow uncertainty and parameter sensitivity, providing faster computational speeds compared to SFM and FPCE. Ultimately, the SPCE approach can benefit ensemble streamflow forecasting studies by quickly providing accurate information in real time.
Article
Engineering, Aerospace
John A. Schaefer, Andrew W. Cary, Mori Mani, Thomas A. Grandine, Christopher J. Roy, Heng Xiao
Summary: In recent years, there has been a substantial increase in demand for stochastic engineering results, with advancements in computing technology and statistical methods making uncertainty quantification studies feasible. While uncertainty can be quantified at specific locations, the challenge remains to interpolate or extrapolate this information to predict uncertainty at untested locations.
Article
Engineering, Multidisciplinary
Jingfei Liu, Chao Jiang
Summary: In this paper, a deep kernel polynomial chaos expansion (DKPCE) is proposed as a surrogate model for high dimensional uncertainty propagation. The novel network model connects deep neural network (DNN) and polynomial chaos expansion (PCE), allowing control of the PCE layer's input dimensionality by restricting the number of neurons in the feature layer. The back-propagation algorithm is employed for computing all the parameters of DKPCE, enabling dimension reduction and modeling process simultaneously. A data-driven method is implemented during the modeling process to compute the orthogonal polynomial bases within the PCE layer.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Engineering, Chemical
Jeongeun Son, Yuncheng Du
Summary: This paper introduces an algorithm that combines polynomial chaos expansion (PCE) with generalized dimension reduction method (gDRM) for efficient uncertainty quantification (UQ) in the presence of many uncertainties. The algorithm builds surrogate models of uncertainty based on a standard distribution, and can handle complex functions defined by many uncertainties. The accuracy and efficiency of the algorithm are demonstrated through examples in structural reliability analysis, showing promise for addressing UQ in complex control systems.
Article
Engineering, Civil
Syfur Rahman, Mohammad Jamal Khattak, Bikash Adhikari, Sambodh Adhikari
Summary: A discrete element model was developed to simulate cemented soil mixtures with different micro bond intensities, and the calibrated micro properties were used to accurately predict the stress-strain behavior. The simulations showed the distribution of forces and microdamage at different loading stages, and the agreement between simulation and actual behavior was good, providing new insights into the mechanical properties of the mixtures.
TRANSPORTATION GEOTECHNICS
(2021)
Article
Computer Science, Interdisciplinary Applications
S. Chakraborty, S. Adhikari
Summary: Digital twin technology has great potential in various industrial sectors, but practical adoptions have been hindered by a lack of application-specific details. This study proposes a digital twin framework for linear single-degree-of-freedom structural dynamic systems, involving a physics-based nominal model and a data-driven machine learning model.
COMPUTERS & STRUCTURES
(2021)
Article
Engineering, Multidisciplinary
Tanmoy Chatterjee, Michael I. Friswell, Sondipon Adhikari, Rajib Chowdhury
Summary: This article aims to minimize computational requirements of meta-model assisted RDO by developing a global two-layered approximation technique. The approach eliminates model building and Monte Carlo simulation, demonstrating the potential to yield robust optimal solutions in real-life applications with minimal computational cost.
ENGINEERING OPTIMIZATION
(2022)
Article
Materials Science, Multidisciplinary
S. K. Singh, A. Banerjee, R. K. Varma, S. Adhikari, S. Das
Summary: This paper presents an analytical study on computing natural frequencies and in-plane deflections caused by static forces in panel walls using various theories. The study emphasizes the unique features of the Micropolar-Cosserat theory and its comparison with finite element analysis for simulating panel behavior.
MECHANICS OF ADVANCED MATERIALS AND STRUCTURES
(2022)
Article
Materials Science, Multidisciplinary
Sondipon Adhikari, Arnab Banerjee
Summary: A mechanical approach utilizing inertial amplifiers with cantilever piezoelectric vibration energy harvesters has been proposed to address challenges in harvesting more power from low-frequency and broadband random excitations. Optimal tuning of different parameters of the inertial amplifiers can significantly increase harvested power, allowing for five times more power to be harvested at a 50% lower frequency under harmonic excitation and ten times more power under random broadband excitation.
JOURNAL OF INTELLIGENT MATERIAL SYSTEMS AND STRUCTURES
(2022)
Article
Mechanics
S. Mukherjee, S. Adhikari
Summary: The paper proposes an analytical framework to analyze and quantify the elastic properties of two-dimensional hexagonal lattices with curved elements. It is found that curved beam elements significantly increase the flexibility of the lattice and expand the design space. The analytical approach and expressions provided in the paper offer an efficient framework for the analysis and design of curved lattice materials.
COMPOSITE STRUCTURES
(2022)
Article
Engineering, Mechanical
S. Adhikari, S. Chakraborty
Summary: This paper aims to develop computationally efficient methods for random eigenvalue problems arising in the dynamics of multi-degree-of-freedom systems, by projecting random eigenvectors onto the basis spanned by deterministic eigenvectors and simplifying the overall approach using an iterative technique.
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2022)
Article
Materials Science, Multidisciplinary
V Gupta, B. Bhattacharya, S. Adhikari
Summary: This study presents a lattice-based hourglass metastructure with excellent mechanical properties and energy absorption capacity. The uniaxial compressive response and energy absorption capacity of this structure were investigated through theoretical, simulation, and experimental methods. The results show that the auxetic-based hourglass structure has the highest energy absorption efficiency.
EXPERIMENTAL MECHANICS
(2022)
Article
Engineering, Mechanical
S. Adhikari, S. Mukherjee
Summary: This paper proposes an analytical method based on Castigliano's approach to obtain the exact closed-form expression of the stiffness matrix for beams with stochastic distributed parameters. It is shown that the conventional stochastic element stiffness matrix is a first-order perturbation approximation to the exact expression.
PROBABILISTIC ENGINEERING MECHANICS
(2022)
Article
Engineering, Civil
Sudip Chowdhury, Arnab Banerjee, Sondipon Adhikari
Summary: This paper studies the optimal design of inertial amplifier base isolators (IABI) for mitigating the dynamic response of multi-storey buildings under base excitations. The H-2 optimization method is used to obtain closed-form expressions for the optimal design parameters of IABI. The effectiveness of these expressions is evaluated by comparing the frequency and time domain responses of isolated structures to those of uncontrolled structures. The results show that the response reduction capacity of the optimal inertial amplifier base isolator is increased by 50% to 60% compared to traditional base isolators.
INTERNATIONAL JOURNAL OF STRUCTURAL STABILITY AND DYNAMICS
(2023)
Article
Instruments & Instrumentation
A. Singh, T. Mukhopadhyay, S. Adhikari, B. Bhattacharya
Summary: This paper proposes a novel hybrid lattice structure that allows contactless active modulation of Young's modulus and Poisson's ratio. By incorporating magnetostrictive patches, the mechanical properties of the lattice structure can be controlled remotely using a magnetic field. The study reveals that extreme on-demand modulation of Young's modulus and Poisson's ratio is achievable by controlling the magnetic field.
SMART MATERIALS AND STRUCTURES
(2022)
Article
Engineering, Mechanical
Sudip Chowdhury, Arnab Banerjee, Sondipon Adhikari
Summary: This paper introduces a method of combining negative stiffness devices with inerters to traditional base isolators and tuned mass dampers. The optimal design parameters of these novel passive vibration dampers are derived using H2 and H & INFIN; optimization methods. The results show that the optimized negative stiffness inerter-based base isolators and tuned mass dampers outperform traditional base isolators and tuned mass dampers in terms of dynamic response reduction capacity.
INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES
(2023)
Article
Acoustics
Sudip Chowdhury, Arnab Banerjee, Sondipon Adhikari
Summary: This paper introduces the inertial amplifier viscoelastic tuned mass dampers (IAVTMD) and presents the mathematical formulations for optimal design parameters. The dynamic response reduction capacity of IAVTMD is significantly superior to conventional tuned mass dampers, with an improvement ranging from 20.87% to 26.47% for H-2 optimization and 15.48% for H-8 optimization. In addition, the optimized IAVTMD outperforms tuned mass damper inerters (TMDI) with improved dynamic response reduction capacity by 6.94% for H-2 optimization and 23.29% for H-8 optimization. The closed-form expressions for optimal design parameters are effective for practical applications.
JOURNAL OF VIBRATION AND CONTROL
(2023)
Article
Mechanics
Sudip Chowdhury, Arnab Banerjee, Sondipon Adhikari
Summary: This article introduces the concepts of additional inerter-based viscoelastic mass dampers (AIVMD) and additional viscoelastic mass damper inerters (AVMDI). H-2 and H-infinity optimization schemes are used to derive the optimal closed-form solutions for these dampers analytically. A parametric study is conducted to investigate the sensitivity of the optimal design parameters with other system parameters. The results suggest that higher damper mass ratio, inerter mass ratio, and stiffness ratio are recommended for designing optimal novel dampers with robust vibration reduction capacities.
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2023)
Article
Engineering, Civil
Sudip Chowdhury, Arnab Banerjee, Sondipon Adhikari
Summary: This paper introduces the design of negative stiffness inerter-based base isolators, which enhance the dynamic response reduction capacity of conventional base isolators by installing negative stiffness devices and inerters. These novel isolators have been installed at the base of multi-storey buildings to mitigate their dynamic responses during vibration.
Article
Engineering, Mechanical
R. Allahvirdizadeh, A. Andersson, R. Karoumi
Summary: The operational safety of high-speed trains on ballasted bridges relies on preventing ballast destabilization. This study explores the impact of epistemic uncertainties on the system using ISRA. Neglecting these uncertainties can lead to overestimation of permissible train speeds and reduced system safety.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Lujie Shi, Leila Khalij, Christophe Gautrelet, Chen Shi, Denis Benasciutti
Summary: This study proposes an innovative Two-phase method based on the Langlie method and the D-optimality criterion to overcome the intrinsic shortcomings of the staircase method used in estimating the fatigue limit distribution. Through simulation-based study, it is demonstrated that the proposed method improves the estimation performance for the mean and standard deviation of the fatigue limit distribution.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Axay Thapa, Atin Roy, Subrata Chakraborty
Summary: This article compares different metamodeling approaches for reliability analysis of tunnels to evaluate their performance. The study found that Kriging and support vector regression models perform well in estimating the reliability of underground tunnels.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Jiaqi Wang, Zhenzhou Lu, Lu Wang
Summary: This paper proposes an efficient method to estimate the FP-GS using reliability updating, avoiding the time-consuming double-loop structure analysis. By utilizing the likelihood function and adaptive Kriging model, the unconditional FP and all conditional FPs can be estimated simultaneously.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Jiaxu Li, Ming Liu, Xu Yan, Qianting Yang
Summary: Wind pressure is essential for architectural design, and this study found that using different probabilistic distribution models can improve the accuracy of reference wind pressure calculation. In the research conducted in Liaoning Province, the extreme value type III model and moment method achieved the best fit. Additionally, probability density functions for wind speed and wind direction were established for further analysis of wind pressure.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
Article
Engineering, Mechanical
Yufan Cheng, Xinchen Zhuang, Tianxiang Yu
Summary: This paper proposes a time-dependent kinematic reliability analysis method that takes into account the truncated random variables and joint clearances, effectively addressing the issues of dimension variables and correlation between joint clearance variables. The proposed method transforms time-dependent reliability into time-independent reliability, greatly reducing computational complexity and obtaining upper and lower bounds of failure probability.
PROBABILISTIC ENGINEERING MECHANICS
(2024)
