HKU Scholars Hubhttp://hub.hku.hkThe DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Sun, 03 Dec 2023 07:26:09 GMT2023-12-03T07:26:09Z50421- On the evaluation of multiple failure probability curves in reliability analysis with multiple performance functionshttp://hdl.handle.net/10722/296269Title: On the evaluation of multiple failure probability curves in reliability analysis with multiple performance functions
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © 2017 Elsevier Ltd Many systems have multiple failure modes that result in multiple performance functions. In this paper, a new stochastic simulation based approach is proposed for evaluation of multiple failure probability curves in a reliability problem with multiple performance functions. The state-of-the-art stochastic simulation based techniques, such as subset simulation and auxiliary domain method, are efficient in evaluating a failure probability curve but only consider a single performance function. Standard Monte Carlo simulation is robust to the type and dimension of the problem and is applicable to evaluate multiple failure probability curves for a problem with multiple performance functions but is computationally expensive especially while estimating small probabilities. The proposed approach for simultaneous consideration of multiple performance functions generalizes the subset simulation and is an improvement of the generalized subset simulation. The output of an analysis using the proposed approach is multiple failure probability curves with each corresponding to one performance function. The proposed approach is robust with respect to the dimension of the failure probability integral, model complexity, the degree of nonlinearity, number of performance functions, and efficient in cases involving the computation of small failure probabilities. The effectiveness and efficiency of the proposed approach are demonstrated by three numerical examples.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10722/2962692017-01-01T00:00:00Z
- On the long-term fatigue assessment of mooring and riser systemshttp://hdl.handle.net/10722/295999Title: On the long-term fatigue assessment of mooring and riser systems
Authors: Low, Ying Min; Cheung, Sai Hung
Abstract: Mooring lines and risers are exposed to numerous sea states during its service life, thus a fatigue assessment should account for the long-term wave condition which is typically represented by the joint statistics of the significant waveheight and a characteristic period. Since it is unfeasible to consider all possible sea states, a common practice, as recommended by design codes, is the lumping of sea states into blocks. However, there are neither guidelines nor consensus on an effective blocking strategy, and the level of discrepancy arising from blocking has not been investigated. In fact, the present problem can be treated by a variety of techniques that are fairly standard within the specialist field of uncertainty analysis, but these methods are foreign to many practicing engineers. Therefore, this paper seeks to make the methodology accessible to the wider offshore engineering community. Six existing approaches are implemented on an illustrative floating system and evaluated for accuracy and efficiency. This paper also presents a novel customized approach that is fast and precise. The approach adopts a multi-peaked third-order asymptotic approximation that is rarely seen in the literature. The peaks are located by a tailored optimization algorithm that exploits the problem peculiarities. © 2012 Elsevier Ltd. All rights reserved.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2959992012-01-01T00:00:00Z
- Multi-objective optimal design for flood risk management with resilience objectiveshttp://hdl.handle.net/10722/296163Title: Multi-objective optimal design for flood risk management with resilience objectives
Authors: Su, Hsin Ting; Cheung, Sai Hung; Lo, Edmond Yat Man
Abstract: © 2018, Springer-Verlag GmbH Germany, part of Springer Nature. In flood risk management, the divergent concept of resilience of a flood defense system cannot be fully defined quantitatively by one indicator and multiple indicators need to be considered simultaneously. In this paper, a multi-objective optimization (MOO) design framework is developed to determine the optimal protection level of a levee system based on different resilience indicators that depend on the probabilistic features of the flood damage cost arising under the uncertain nature of rainfalls. An evolutionary-based MOO algorithm is used to find a set of non-dominated solutions, known as Pareto optimal solutions for the optimal protection level. The objective functions, specifically resilience indicators of severity, variability and graduality, that account for the uncertainty of rainfall can be evaluated by stochastic sampling of rainfall amount together with the model simulations of incurred flood damage estimation for the levee system. However, these model simulations which usually require detailed flood inundation simulation are computationally demanding. This hinders the wide application of MOO in flood risk management and is circumvented here via a surrogate flood damage modeling technique that is integrated into the MOO algorithm. The proposed optimal design framework is applied to a levee system in a central basin of flood-prone Jakarta, Indonesia. The results suggest that the proposed framework enables the application of MOO with resilience objectives for flood defense system design under uncertainty and solves the decision making problems efficiently by drastically reducing the required computational time.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10722/2961632018-01-01T00:00:00Z
- Auxiliary domain method for solving multi-objective dynamic reliability problems for nonlinear structureshttp://hdl.handle.net/10722/296036Title: Auxiliary domain method for solving multi-objective dynamic reliability problems for nonlinear structures
Authors: Katafygiotis, Lambros; Moan, Torgeir; Cheung, Sai Hung
Abstract: A novel methodology, referred to as Auxiliary Domain Method (ADM), allowing for a very efficient solution of nonlinear reliability problems is presented. The target nonlinear failure domain is first populated by samples generated with the help of a Markov Chain. Based on these samples an auxiliary failure domain (AFD), corresponding to an auxiliary reliability problem, is introduced. The criteria for selecting the AFD are discussed. The emphasis in this paper is on the selection of the auxiliary linear failure domain in the case where the original nonlinear reliability problem involves multiple objectives rather than a single objective. Each reliability objective is assumed to correspond to a particular response quantity not exceeding a corresponding threshold. Once the AFD has been specified the method proceeds with a modified subset simulation procedure where the first step involves the direct simulation of samples in the AFD, rather than standard Monte Carlo simulation as required in standard subset simulation. While the method is applicable to general nonlinear reliability problems herein the focus is on the calculation of the probability of failure of nonlinear dynamical systems subjected to Gaussian random excitations. The method is demonstrated through such a numerical example involving two reliability objectives and a very large number of random variables. It is found that ADM is very efficient and offers drastic improvements over standard subset simulation, especially when one deals with low probability failure events.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/2960362007-01-01T00:00:00Z
- Stochastic simulation algorithm for robust reliability updating of structural dynamic systems based on incomplete modal datahttp://hdl.handle.net/10722/296164Title: Stochastic simulation algorithm for robust reliability updating of structural dynamic systems based on incomplete modal data
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © 2017 American Society of Civil Engineers. This paper presents an approach for updating the robust structural reliability that any particular response of a structural dynamic system will not reach some specific failure or unfavorable state when it is subjected to future stochastic excitation. In particular, the updating approach is based on incomplete modal data identified from the structural system. Uncertainties arising from structural modeling and modeling of the stochastic excitation that the structure will experience during its lifetime are considered. The proposed approach integrates the Gibbs sampler for Bayesian model updating and subset simulation for failure probability computation. A new efficient approach for conditional sampling called a constrained multigroup Metropolis within Gibbs (CMMG) sampling algorithm is developed by the authors. Another appealing feature of the proposed method is that it provides not only the exceedance probability estimates but also conditional samples that allow in-depth failure analysis in a single simulation run. The proposed method provides a substantial improvement in efficiency over estimators based on crude Monte Carlo simulation (MCS) for the updated robust reliability and is robust to the number of random variables and uncertain parameters and the amount of modal data involved in the problem. The effectiveness and efficiency of the proposed approach are shown by two illustrative examples.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10722/2961642017-01-01T00:00:00Z
- Domain decomposition method for calculating the failure probability of linear dynamic systems subjected to gaussian stochastic loadshttp://hdl.handle.net/10722/296034Title: Domain decomposition method for calculating the failure probability of linear dynamic systems subjected to gaussian stochastic loads
Authors: Katafygiotis, Lambros; Cheung, Sai Hung
Abstract: In this paper the problem of calculating the probability of failure of linear dynamic systems subjected to random vibrations is considered. This is a very important and challenging problem in structural reliability. The failure domain in this case can be described as a union of linear failure domains whose boundaries are hyperplanes. Each linear limit state function can be completely described by its own design point, which can be analytically determined, allowing for an exact analytical calculation of the corresponding failure probability. The difficulty in calculating the overall failure probability arises from the overlapping of the different linear failure domains, the degree of which is unknown and needs to be determined. A novel robust reliability methodology, referred to as the domain decomposition method (DDM), is proposed to calculate the probability that the response of a linear system exceeds specified target thresholds. It exploits the special structure of the failure domain, given by the union of a large number of linear failure regions, to obtain an extremely efficient and highly accurate estimate of the failure probability. The number of dynamic analyses to be performed in order to determine the failure probability is as low as the number of independent random excitations driving the system. Furthermore, calculating the reliability of the same structure under different performance objectives does not require any additional dynamic analyses. Two numerical examples are given demonstrating the proposed method, both of which show that the method offers dramatic improvement over standard Monte Carlo simulations, while a comparison with the ISEE algorithm shows that the DDM is at least as efficient as the ISEE. © ASCE.
Sun, 01 Jan 2006 00:00:00 GMThttp://hdl.handle.net/10722/2960342006-01-01T00:00:00Z
- Cyber-physical systems approach for smart grid data standardization for electricity infrastructurehttp://hdl.handle.net/10722/296193Title: Cyber-physical systems approach for smart grid data standardization for electricity infrastructure
Authors: Balijepalli, V. S.K. Murthy; Cheung, Sai Hung
Abstract: © 2019 IEEE. Standardization is one of the focused areas for establishing smart grid industry. Understanding the power system data standards is limited to the industry niche segment due to lack of proper open-source tools and established practices. This paper highlights the necessity for data standardization in the context of smart grids using cyber-physical systems approach for the electricity infrastructure. Power system common information models are utilized and suitable extensions have been proposed. A case study on modeling and designing Demand Response application using this approach is presented. This paper also presents a framework in smart grid data standardization based on the experiences and insights gained through this research work.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10722/2961932019-01-01T00:00:00Z
- An efficient surrogate-aided importance sampling framework for reliability analysishttp://hdl.handle.net/10722/296191Title: An efficient surrogate-aided importance sampling framework for reliability analysis
Authors: Liu, Wang Sheng; Cheung, Sai Hung; Cao, Wen Jun
Abstract: © 2019 Elsevier Ltd Surrogates in lieu of expensive-to-evaluate performance functions can accelerate the reliability analysis greatly. This paper proposes a new two-stage framework for surrogate-aided reliability analysis named Surrogates for Importance Sampling (S4IS). In the first stage, a coarse surrogate is built to gain the information about failure regions. The second stage zooms into the important regions and improves the accuracy of the failure probability estimator by adaptively selecting support points. The learning functions are proposed to guide the selection of support points such that the exploration and exploitation can be dynamically balanced. As a generic framework, S4IS has the potential to incorporate different types of surrogates (Gaussian Processes, Support Vector Machines, Neural Network, etc.). The effectiveness and efficiency of S4IS are validated by five illustrative examples, which involve system reliability, highly nonlinear limit-state functions, small failure probability and moderately high dimensionality. The implementation of S4IS is made available to download at https://sites.google.com/site/josephsaihungcheung/.
Tue, 01 Jan 2019 00:00:00 GMThttp://hdl.handle.net/10722/2961912019-01-01T00:00:00Z
- A Stochastic Simulation Algorithm for Updating Robust Reliability of Nonlinear Structural Dynamic Systems Based on Incomplete Modal Datahttp://hdl.handle.net/10722/296105Title: A Stochastic Simulation Algorithm for Updating Robust Reliability of Nonlinear Structural Dynamic Systems Based on Incomplete Modal Data
Authors: Cheung, Sai Hung; Bansal, Sahil
Abstract: © 2014 American Society of Civil Engineers. In this paper, we are interested in using the incomplete modal data identified from the system to update the robust failure probability that any particular response of a nonlinear structural dynamic system exceeds a specified threshold during the time when it is subjected to future stochastic dynamic excitation. Uncertainties from structural modeling -the modeling of the stochastic excitation that the structure will experience during its lifetime -And any other uncertainties can all be taken into account. A new efficient approach based on stochastic simulation methods, which is very robust to the number of random variables and uncertain model parameters, is proposed to update the robust reliability of the structure. The efficiency and effectiveness of the proposed approach are illustrated by a numerical example involving a nonlinear structural dynamic model of a building.
Wed, 01 Jan 2014 00:00:00 GMThttp://hdl.handle.net/10722/2961052014-01-01T00:00:00Z
- Probabilistic seismic loss estimation by stochastic simulation algorithmshttp://hdl.handle.net/10722/296264Title: Probabilistic seismic loss estimation by stochastic simulation algorithms
Authors: Cheung, S. H.; Bansal, S.
Abstract: Prior estimation of losses in a structure due to future uncertain earthquakes, especially those which can lead to disastrous consequences, is essential to reduce potential losses and assist recovery. In this paper, we are particularly interested in the evaluation of seismic loss probability distribution including its tail by evaluating the seismic loss exceedance probability. Taking into account uncertainties from structural modeling, the modeling of future uncertain excitation and the models for damage and loss analysis, the evaluation of the exceedance probability will unavoidably involve the computation of high-dimensional integrals. A new method based on stochastic simulation algorithms is proposed to compute such integrals more efficiently. The effectiveness and efficiency of the proposed method are shown by an illustrative example involving a multi-storey inelastic building. © 2013 Taylor & Francis Group, London.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2962642013-01-01T00:00:00Z
- A new stochastic simulation algorithm for updating robust reliability of linear structural dynamic systemshttp://hdl.handle.net/10722/296089Title: A new stochastic simulation algorithm for updating robust reliability of linear structural dynamic systems
Authors: Cheung, S. H.; Bansal, S.
Abstract: It is of great interest to assess during the operation of a dynamic system whether it is expected to satisfy specified performance objectives.To do this, the failure probability (or its complement, robust reliability) of the system when it is subjected to dynamic excitation is computed. The word 'failure' is used here to refer to unsatisfactory performance of the system. In this paper, we are interested in using system data to update the robust failure probability that any particular response of a linear structural dynamic system exceeds a specified threshold during the time when the system is subjected to future Gaussian dynamic excitation. Computation of the robust reliability takes into account uncertainties from structural modeling in addition to the modeling of the uncertain excitation that the structure will experience during its lifetime. The updating is based on partial modal data from the structure. By exploiting the properties of linear dynamics, a new approach based on stochastic simulation methods is proposed, to update the robust reliability of the structure. The efficiency of the proposed approach is illustrated by a numerical example involving a linear elastic structural model of a building. © 2013 Taylor & Francis Group, London.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2960892013-01-01T00:00:00Z
- Bayesian model comparison and selection for quantifying uncertainty in active graphite nitridationhttp://hdl.handle.net/10722/296247Title: Bayesian model comparison and selection for quantifying uncertainty in active graphite nitridation
Authors: Miki, Kenji; Upadhyay, Rochan R.; Sahni, Onkar; Cheung, Sai H.
Abstract: In this paper, two stochastic model classes corresponding to different choices of (modeling and measurement) error structure are considered and compared using Bayesian framework. A single deterministic physical model of active graphite nitridation is embedded within these stochastic model classes, where estimation of surface reaction efficiency of graphite with atomic nitrogen is of primary interest. These model classes differ in the covariance matrix structure that is used in the uncertainty model to represent uncertainties associated with the physical model and experimental measurements. First model class (M1) is based on independent normal distributions assuming error to be uncorrelated between different data points whereas the second class (M2) uses γ-exponential covariance function to correlate error in the same data quantity among different data points. For each model class, Bayesian inference is used to estimate the posterior probabilities of the physical model parameters, stochastic model parameters as well as of the candidate stochastic models. Model comparison and selection is then applied based on two measures including Bayesian evidence and Bayesian information criterion (BIC), and deviance information criterion (DIC). Both measures suggest the second stochastic model class (M2) to be selected indicating that there is a correlation between errors in the same data quantity among different data points. However, with the second model class the range of uncertainty in surface reaction efficiency is estimated to be higher, which is consistent with the large scatter seen in the reported values. © 2012 by Kenji Miki.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2962472012-01-01T00:00:00Z
- Application of spherical subset simulation method and auxiliary domain method on a benchmark reliability studyhttp://hdl.handle.net/10722/296230Title: Application of spherical subset simulation method and auxiliary domain method on a benchmark reliability study
Authors: Katafygiotis, L. S.; Cheung, S. H.
Abstract: This paper addresses a benchmark study designed to evaluate the performance of various methods in calculating the reliability of large systems. In particular, this paper focuses on evaluating two reliability methods recently proposed by the authors, referred to as spherical subset simulation (S3) and auxiliary domain method (ADM). S3 is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities associated with each of these subregions. The probability of each subregion is calculated as a product of factors. These factors can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. The generation of such samples is achieved through Markov Chain Monte Carlo (MCMC) simulations using a MCMC algorithm proposed by the authors. The proposed method is very robust and is suitable for treating general high-dimensional problems such as the given benchmark problems. ADM is applicable to reliability problems involving deterministic dynamic systems subjected to stochastic excitation. The first step in ADM involves the determination of an auxiliary failure domain (AFD). The choice of the AFD is based on preliminary MCMC simulations in the target failure domain. It must be noted that although the AFD is chosen to be specified as a union of linear failure domains, the method does not assume any restriction with respect to the target failure domain, which is assumed to be generally non-linear. Once the AFD is determined, the ADM proceeds with a modified subset simulation procedure where the first step involves the direct simulation of points in the AFD. This is in contrast to standard subset simulation (SSM) where the first step involves standard Monte Carlo Simulations. The number of steps and the computational effort required by ADM, assuming an appropriate AFD is chosen, can be smaller than that required by SSM. Results for the benchmark problems show that both S3 and ADM are efficient for treating high dimensional reliability problems. © 2006 Elsevier Ltd. All rights reserved.
Mon, 01 Jan 2007 00:00:00 GMThttp://hdl.handle.net/10722/2962302007-01-01T00:00:00Z
- Stochastic sampling using moving least squares response surface approximationshttp://hdl.handle.net/10722/296242Title: Stochastic sampling using moving least squares response surface approximations
Authors: Taflanidis, Alexandros A.; Cheung, Sai Hung
Abstract: This work discusses the simulation of samples from a target probability distribution which is related to the response of a system model that is computationally expensive to evaluate. Implementation of surrogate modeling, in particular moving least squares (MLS) response surface methodologies, is suggested for efficient approximation of the model response for reduction of the computational burden associated with the stochastic sampling. For efficient selection of the MLS weights and improvement of the response surface approximation accuracy, a novel methodology is introduced, based on information about the sensitivity of the sampling process with respect to each of the model parameters. An approach based on the relative information entropy is suggested for this purpose, and direct evaluation from the samples available from the stochastic sampling is discussed. A novel measure is also introduced for evaluating the accuracy of the response surface approximation in terms relevant to the stochastic sampling task. © 2011 Elsevier Ltd. All rights reserved.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2962422012-01-01T00:00:00Z
- A bayesian approach for constructing ensemble neural networkhttp://hdl.handle.net/10722/296081Title: A bayesian approach for constructing ensemble neural network
Authors: Cheung, Sai Hung; Zhang, Yun; Zhao, Zhiye
Abstract: Ensemble neural networks (ENNs) are commonly used in many engineering applications due to its better generalization properties compared with a single neural network (NN). As the NN architecture has a significant influence on the generalization ability of an NN, it is crucial to develop a proper algorithm to design the NN architecture. In this paper, an ENN which combines the component networks by using the Bayesian approach and stochastic modelling is proposed. The cross validation data set is used not only to stop the network training, but also to determine the weights of the component networks. The proposed ENN searches the best structure of each component network first and employs the Bayesian approach as an automating design tool to determine the best combining weights of the ENN. Peak function is used to assess the accuracy of the proposed ensemble approach. The results show that the proposed ENN outperforms ENN obtained by simple averaging and the single NN. Copyright © 2012 SciTePress - Science and Technology Publications.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2960812012-01-01T00:00:00Z
- A new stochastic simulation algorithm for updating robust reliability of linear structural dynamic systems subjected to future Gaussian excitationshttp://hdl.handle.net/10722/296157Title: A new stochastic simulation algorithm for updating robust reliability of linear structural dynamic systems subjected to future Gaussian excitations
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © 2017 Elsevier B.V. In this paper, we are interested in using system response data to update the robust failure probability that any particular response of a linear structural dynamic system exceeds a specified threshold during the time when the system is subjected to future Gaussian dynamic excitations. Computation of the robust reliability takes into account uncertainties from structural modeling in addition to the modeling of the uncertain excitations that the structure will experience during its lifetime. In partial, modal data from the structure are used as the data for the updating. By exploiting the properties of linear dynamics, a new approach based on stochastic simulation methods is proposed to update the robust reliability of the structure. The proposed approach integrates the Gibbs sampler for Bayesian model updating and Subset Simulation for failure probability computation. A new efficient approach for conditional sampling called ‘Constrained Metropolis within Gibbs sampling’ algorithm is developed by the authors. It is robust to the number of uncertain parameters and random variables and the dimension of modal data involved in the problem. The effectiveness and efficiency of the proposed approach are illustrated by two numerical examples involving linear elastic dynamic systems.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10722/2961572017-01-01T00:00:00Z
- Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parametershttp://hdl.handle.net/10722/296050Title: Bayesian model updating using hybrid Monte Carlo simulation with application to structural dynamic models with many uncertain parameters
Authors: Cheung, Sai Hung; Beck, James L.
Abstract: In recent years, Bayesian model updating techniques based on measured data have been applied to system identification of structures and to structural health monitoring. A fully probabilistic Bayesian model updating approach provides a robust and rigorous framework for these applications due to its ability to characterize modeling uncertainties associated with the underlying structural system and to its exclusive foundation on the probability axioms. The plausibility of each structural model within a set of possible models, given the measured data, is quantified by the joint posterior probability density function of the model parameters. This Bayesian approach requires the evaluation of multidimensional integrals, and this usually cannot be done analytically. Recently, some Markov chain Monte Carlo simulation methods have been developed to solve the Bayesian model updating problem. However, in general, the efficiency of these proposed approaches is adversely affected by the dimension of the model parameter space. In this paper, the Hybrid Monte Carlo method is investigated (also known as Hamiltonian Markov chain method), and we show how it can be used to solve higher-dimensional Bayesian model updating problems. Practical issues for the feasibility of the Hybrid Monte Carlo method to such problems are addressed, and improvements are proposed to make it more effective and efficient for solving such model updating problems. New formulae for Markov chain convergence assessment are derived. The effectiveness of the proposed approach for Bayesian model updating of structural dynamic models with many uncertain parameters is illustrated with a simulated data example involving a ten-story building that has 31 model parameters to be updated. © 2009 ASCE.
Thu, 01 Jan 2009 00:00:00 GMThttp://hdl.handle.net/10722/2960502009-01-01T00:00:00Z
- Bayesian uncertainty analysis with applications to turbulence modelinghttp://hdl.handle.net/10722/296069Title: Bayesian uncertainty analysis with applications to turbulence modeling
Authors: Cheung, Sai Hung; Oliver, Todd A.; Prudencio, Ernesto E.; Prudhomme, Serge; Moser, Robert D.
Abstract: In this paper, we apply Bayesian uncertainty quantification techniques to the processes of calibrating complex mathematical models and predicting quantities of interest (QoI's) with such models. These techniques also enable the systematic comparison of competing model classes. The processes of calibration and comparison constitute the building blocks of a larger validation process, the goal of which is to accept or reject a given mathematical model for the prediction of a particular QoI for a particular scenario. In this work, we take the first step in this process by applying the methodology to the analysis of the SpalartAllmaras turbulence model in the context of incompressible, boundary layer flows. Three competing model classes based on the SpalartAllmaras model are formulated, calibrated against experimental data, and used to issue a prediction with quantified uncertainty. The model classes are compared in terms of their posterior probabilities and their prediction of QoI's. The model posterior probability represents the relative plausibility of a model class given the data. Thus, it incorporates the model's ability to fit experimental observations. Alternatively, comparing models using the predicted QoI connects the process to the needs of decision makers that use the results of the model. We show that by using both the model plausibility and predicted QoI, one has the opportunity to reject some model classes after calibration, before subjecting the remaining classes to additional validation challenges. © 2011 Elsevier Ltd. All rights reserved.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/10722/2960692011-01-01T00:00:00Z
- Stochastic simulation based reliability analysis with multiple performance objective functionshttp://hdl.handle.net/10722/296110Title: Stochastic simulation based reliability analysis with multiple performance objective functions
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © Springer India 2015. In this paper, a stochastic simulation approach is proposed to estimate small failure probabilities of multiple limit states. The proposed approach allows for the simultaneous consideration of multiple performance functions and the corresponding thresholds. The approach modifies the stochastic subset simulation algorithm that can efficiently compute small failure probabilities. The proposed approach is robust with respect to the dimension of the failure probability integral, model complexity and nonlinearity. The effectiveness and efficiency of the proposed method are illustrated by a numerical example involving a structural dynamic system subjected to future earthquake excitations modeled as a stochastic process and the results are compared to those obtained using crude Monte Carlo simulation.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10722/2961102015-01-01T00:00:00Z
- Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System Datahttp://hdl.handle.net/10722/296052Title: Calculation of Posterior Probabilities for Bayesian Model Class Assessment and Averaging from Posterior Samples Based on Dynamic System Data
Authors: Cheung, Sai Hung; Beck, James L.
Abstract: In recent years, Bayesian model updating techniques based on dynamic data have been applied in system identification and structural health monitoring. Because of modeling uncertainty, a set of competing candidate model classes may be available to represent a system and it is then desirable to assess the plausibility of each model class based on system data. Bayesian model class assessment may then be used, which is based on the posterior probability of the different candidates for representing the system. If more than one model class has significant posterior probability, then Bayesian model class averaging provides a coherent mechanism to incorporate all of these model classes in making probabilistic predictions for the system response. This Bayesian model assessment and averaging requires calculation of the evidence for each model class based on the system data, which requires the evaluation of a multi-dimensional integral involving the product of the likelihood and prior defined by the model class. In this article, a general method for calculating the evidence is proposed based on using posterior samples from any Markov Chain Monte Carlo algorithm. The effectiveness of the proposed method is illustrated by Bayesian model updating and assessment using simulated earthquake data from a ten-story nonclassically damped building responding linearly and a four-story building responding inelastically. © 2010 Computer-Aided Civil and Infrastructure Engineering.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/2960522010-01-01T00:00:00Z
- Wedge simulation method for calculating the reliability of linear dynamical systemshttp://hdl.handle.net/10722/296025Title: Wedge simulation method for calculating the reliability of linear dynamical systems
Authors: Katafygiotis, Lambros; Cheung, Sai Hung
Abstract: In this paper the problem of calculating the probability of failure of linear dynamical systems subjected to random excitations is considered. The failure probability can be described as a union of failure events each of which is described by a linear limit state function. While the failure probability due to a union of non-interacting limit state functions can be evaluated without difficulty, the interaction among the limit state functions makes the calculation of the failure probability a difficult and challenging task. A novel robust reliability methodology, referred to as Wedge-Simulation-Method, is proposed to calculate the probability that the response of a linear system subjected to Gaussian random excitation exceeds specified target thresholds. A numerical example is given to demonstrate the efficiency of the proposed method which is found to be enormously more efficient than Monte Carlo Simulations. © 2004 Elsevier Ltd. All rights reserved.
Thu, 01 Jan 2004 00:00:00 GMThttp://hdl.handle.net/10722/2960252004-01-01T00:00:00Z
- Reliability based design optimization with approximate failure probability function in partitioned design spacehttp://hdl.handle.net/10722/296007Title: Reliability based design optimization with approximate failure probability function in partitioned design space
Authors: Liu, Wang Sheng; Cheung, Sai Hung
Abstract: © 2017 Elsevier Ltd This paper presents an efficient method for reliability-based design optimization (RBDO), which is robust to complex systems involving computationally expensive numerical models and/or a large number of random variables. This novel method belongs to a type of decoupling approaches in which the failure probability function (FPF) is approximated in the partitioned design space. In the setting of augmented reliability formulation, for a specific design configuration, the failure probability of a system is proportional to the probability density value of design variables conditioned on the failure event, thus transforming FPF approximation into a problem of density estimation. In this paper, we partition the design space into several subspaces and then estimate the density of failure samples in each subspace by binning and constructing regression functions. Sufficient failure samples are efficiently generated in each subspace using Markov Chain Monte Carlo method, which guarantees the accuracy of FPF approximation over there and ultimately over the entire design space. Three illustrative examples involving structural systems subjected to static or dynamic loadings are discussed to demonstrate the efficiency and accuracy of the proposed method.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10722/2960072017-01-01T00:00:00Z
- Uncertainty quantification and robust predictive system analysis for high temperature kinetics of HCN/O<inf>2</inf>/Ar mixturehttp://hdl.handle.net/10722/296129Title: Uncertainty quantification and robust predictive system analysis for high temperature kinetics of HCN/O<inf>2</inf>/Ar mixture
Authors: Cheung, Sai Hung; Miki, Kenji; Prudencio, Ernesto; Simmons, Chris
Abstract: © 2016 Elsevier B.V. In this paper, a stochastic system based Bayesian approach is applied to quantify the uncertainties involved in the modeling of the HCN/O2/Ar mixture kinetics proposed by Thielen and Roth (1987). This enables more robust predictions of quantities of interest such as rate coefficients of HCN + Ar → H + CN + Ar and O2 + CN → NCO + O by using a stochastic Arrhenius form calibrated against their experimental data. This Bayesian approach requires the evaluation of multidimensional integrals, which cannot be done analytically. Here a recently developed stochastic simulation algorithm, which allows for efficient sampling in the high-dimensional parameter space, is used. We quantify the uncertainties in the modeling of the HCN/O2/Ar mixture kinetics and in turn the two rate coefficients and the other relevant rate coefficients. The uncertainty in the error including both the experimental measurement error and physical modeling error is also quantified. The effect of the number of uncertain parameters on the uncertainties is investigated.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10722/2961292016-01-01T00:00:00Z
- A two-stage Subset Simulation-based approach for calculating the reliability of inelastic structural systems subjected to Gaussian random excitationshttp://hdl.handle.net/10722/296022Title: A two-stage Subset Simulation-based approach for calculating the reliability of inelastic structural systems subjected to Gaussian random excitations
Authors: Katafygiotis, Lambros; Cheung, Sai Hung
Abstract: This paper presents a methodology for calculating the reliability of inelastic structural systems subjected to Gaussian random excitations. The method adopts a two-stage approach, involving separate calculation of the failure probabilities associated with linear elastic and inelastic structural response. The method exploits the fact that the calculation of failure probabilities associated with a linear problem can be performed extremely efficiently, using minimal computational effort compared to the effort required for solving a corresponding nonlinear problem. The calculation of failure probability associated with inelastic response is performed using a modified Subset Simulation procedure where the first step involves the direct simulation of samples in the inelastic domain rather than standard Monte Carlo simulations as in Standard Subset Simulation. It is demonstrated with a numerical example that the proposed two-stage approach offers significant computational savings over the Standard Subset Simulation approach. © 2004 Published by Elsevier B.V.
Sat, 01 Jan 2005 00:00:00 GMThttp://hdl.handle.net/10722/2960222005-01-01T00:00:00Z
- Stochastic sampling based Bayesian model updating with incomplete modal datahttp://hdl.handle.net/10722/296268Title: Stochastic sampling based Bayesian model updating with incomplete modal data
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © 2016 by Begell House, Inc. In this paper, we are interested in model updating of a linear dynamic system based on incomplete modal data including modal frequencies, damping ratios, and partial mode shapes of some of the dominant modes. To quantify the uncertain-ties and plausibility of the model parameters, a Bayesian approach is developed. The mass and stiffness matrices in the identification model are represented as a linear sum of the contribution of the corresponding mass and stiffness matrices from the individual prescribed substructures. The damping matrix is represented as a sum of the contribution from individual substructures in the case of viscous damping, in terms of mass and stiffness matrices in the case of classical damping (Caughey damping), or a combination of the viscous and classical damping. A Metropolis-within-Gibbs sam-pling based algorithm is proposed that allows for an efficient sampling from the posterior probability distribution. The effectiveness and efficiency of the proposed method are illustrated by numerical examples with complex modes.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10722/2962682016-01-01T00:00:00Z
- A stochastic simulation algorithm for bayesian model updating of linear structural dynamic system with non-classical dampinghttp://hdl.handle.net/10722/296091Title: A stochastic simulation algorithm for bayesian model updating of linear structural dynamic system with non-classical damping
Authors: Cheung, S. H.; Bansal, S.
Abstract: Model updating using measured system dynamic response has a wide range of applications in structural health monitoring and control, response prediction, reliability and risk assessment. In this paper, we are interested in model updating of a linear structural dynamic system with non-classical damping based on incomplete modal data including modal frequencies, damping ratios and partial complex mode shapes of some of the dominant modes. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is considered in which the probability distribution of the model parameters needs to be updated.A new stochastic simulation algorithm is proposed, which allows for an efficient update of the probability distribution of the model parameters. The effectiveness and efficiency of the proposed method are illustrated by a numerical example involving linear structural dynamic system with complex modes. © 2013 Taylor & Francis Group, London.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2960912013-01-01T00:00:00Z
- A subset simulation based approach with modified conditional sampling and estimator for loss exceedance curve computationhttp://hdl.handle.net/10722/296169Title: A subset simulation based approach with modified conditional sampling and estimator for loss exceedance curve computation
Authors: Bansal, Sahil; Cheung, Sai Hung
Abstract: © 2018 Elsevier Ltd A new stochastic simulation-based approach for the evaluation of loss exceedance curve without repeated reliability analyses, and the generation of samples of input random variables and any function of them conditioned on different levels of loss exceedance is proposed for a comprehensive risk and loss analysis, and investigation. The proposed approach involves the modification of the simulation algorithms in the Subset Simulation and the development of new estimators. It allows for a more comprehensive characterization of the probability distribution of the loss including the tail parts due to combinations of scenarios which can lead to extreme and catastrophic consequences. The approach is robust to the number of random variables involved. The effectiveness and efficiency of the proposed method are shown by an illustrative example involving a seismic loss analysis of a multi-story inelastic structure. A stochastic ground motion model coupled with a stochastic nonlinear dynamic model, and probabilistic fragility and loss functions are considered.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/10722/2961692018-01-01T00:00:00Z
- Using Bayesian analysis to quantify uncertainties in the H+O<inf>2</inf>→OH+O reactionhttp://hdl.handle.net/10722/296074Title: Using Bayesian analysis to quantify uncertainties in the H+O<inf>2</inf>→OH+O reaction
Authors: Miki, Kenji; Prudencio, Ernesto E.; Cheung, Sai Hung; Terejanu, Gabriel
Abstract: A stochastic Bayesian approach is applied to investigate the uncertainty in the rate coefficient of H+O2→OH+O (k1) using the latest shock-tube experimental data. We simultaneously calibrate all random variables using a recently developed stochastic simulation algorithm which allows for efficient sampling in the high-dimensional parameter space. We introduce the idea of " irreducible" uncertainty when considering other parameters in the system. Nine stochastic models are constructed depending on the choice of uncertainties, hydrogen concentration, gas temperature, pressure, and rate coefficients of other reactions. The sensitivity analysis of uncertainty in k1 on these uncertainty parameters is performed. It is shown that the introduction of " irreducible" uncertainty does not always increase the uncertainty of k1. In addition, we observe the high sensitivity of uncertainty in k1 to the uncertainty in the measured time-shift. Our results show the strong temperature dependence of the uncertainty in the rate coefficient. © 2013 The Combustion Institute.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2960742013-01-01T00:00:00Z
- Optimal tuned mass damper for a spar buoy floating wind turbine to mitigate fatigue damagehttp://hdl.handle.net/10722/296265Title: Optimal tuned mass damper for a spar buoy floating wind turbine to mitigate fatigue damage
Authors: Gong, Fan Ming; Cheung, Sai Hung
Abstract: © 2016, International Society of Science and Applied Technologies. All rights reserved. Nowadays, there has been an increasing interest in wind energy in deep water. The increasing size of the turbine and the compliance of the floating support structure make the floating wind turbine vulnerable in the harsh environment. Due to the cyclic loads caused by platform pitching and tower bending motion, the fatigue damage at the tower base is relatively high. An optimal design of tuned mass damper (TMD) can reduce the structural load effectively. This paper deals with design optimization of a TMD for a spar buoy floating wind turbine to minimize the fatigue damage at the tower base. A high-fidelity nonlinear simulator is used to model the coupled wind turbine and TMD system. With consideration of uncertainties in environmental conditions and millions of Gaussian white noise random variables accounted for wind turbulences and irregular waves, the objective function becomes a high-dimensional probability integral and Subset Stochastic Optimization (SSO) method is used to solve this optimization problem. A novel acceptance-rejection sampling with subset simulation (ARS-SS) for reliability analysis is proposed for simulating samples required in SSO with less computational cost. In this study, ARS-SS sampling algorithm saves 75% computational cost when compared with the original ARS and the identified optimal TMD can reduce the fatigue damage by 15%.
Fri, 01 Jan 2016 00:00:00 GMThttp://hdl.handle.net/10722/2962652016-01-01T00:00:00Z
- A new gibbs-sampling based algorithm for bayesian model updating of linear dynamic systems with incomplete complex modal datahttp://hdl.handle.net/10722/296083Title: A new gibbs-sampling based algorithm for bayesian model updating of linear dynamic systems with incomplete complex modal data
Authors: Hung, Cheung Sai; Bansal, Sahil
Abstract: Model updating using measured system dynamic response has a wide range of applications in structural health monitoring, control and response prediction. In this paper, we are interested in model updating of a linear structural dynamic system with non-classical damping based on incomplete modal data including modal frequencies, damping ratios, and partial complex mode shapes of some of the dominant modes. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is adopted. A new Gibbs-sampling based algorithm is proposed that allows for an efficient update of the probability distribution of the model parameters. The effectiveness and efficiency of the proposed method are illustrated by a numerical example involving a linear structural dynamic system with complex modes.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2960832013-01-01T00:00:00Z
- Reliability assessment of structural dynamic systems due to earthquake-induced tsunamishttp://hdl.handle.net/10722/296258Title: Reliability assessment of structural dynamic systems due to earthquake-induced tsunamis
Authors: Cheung, S. H.; Shao, Z.
Abstract: Recent earthquake-induced tsunamis in Padang, 2004 and Tohoku, 2011 brought huge losses of lives and properties. Thus, it is of our great interest to quantify the risk to structural dynamic systems due to earthquake-induced tsunamis. Despite continuous advancement in computational simulation of the tsunami and wave-structure interaction modeling, it still remains computationally challenging to evaluate the reliability (or its complement failure probability) of a structural dynamic system when uncertainties related to the system and its modeling are taken into account. The failure of the structure in a tsunami-wave-structural system is defined as any response quantities of the system exceeding specified thresholds during the time when the structure is subjected to dynamic wave impact due to earthquake-induced tsunamis. In this paper, an approach based on a novel integration of the Subset Simulation algorithm and a recently proposed moving least squares response surface approach for stochastic sampling is proposed. The effectiveness of the proposed approach is discussed by comparing its results with those obtained from the Subset Simulation algorithm without using the response surface approach. © 2013 Taylor & Francis Group, London.
Tue, 01 Jan 2013 00:00:00 GMThttp://hdl.handle.net/10722/2962582013-01-01T00:00:00Z
- A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal datahttp://hdl.handle.net/10722/296142Title: A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data
Authors: Cheung, Sai Hung; Bansal, Sahil
Abstract: © 2017 Model updating using measured system dynamic response has a wide range of applications in system response evaluation and control, health monitoring, or reliability and risk assessment. In this paper, we are interested in model updating of a linear dynamic system with non-classical damping based on incomplete modal data including modal frequencies, damping ratios and partial complex mode shapes of some of the dominant modes. In the proposed algorithm, the identification model is based on a linear structural model where the mass and stiffness matrix are represented as a linear sum of contribution of the corresponding mass and stiffness matrices from the individual prescribed substructures, and the damping matrix is represented as a sum of individual substructures in the case of viscous damping, in terms of mass and stiffness matrices in the case of Rayleigh damping or a combination of the former. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is developed. A new Gibbs-sampling based algorithm is proposed that allows for an efficient update of the probability distribution of the model parameters. In addition to the model parameters, the probability distribution of complete mode shapes is also updated. Convergence issues and numerical issues arising in the case of high-dimensionality of the problem are addressed and solutions to tackle these problems are proposed. The effectiveness and efficiency of the proposed method are illustrated by numerical examples with complex modes.
Sun, 01 Jan 2017 00:00:00 GMThttp://hdl.handle.net/10722/2961422017-01-01T00:00:00Z
- Spherical subset simulation (S<sup>3</sup>) for solving non-linear dynamical reliability problemshttp://hdl.handle.net/10722/296068Title: Spherical subset simulation (S<sup>3</sup>) for solving non-linear dynamical reliability problems
Authors: Katafygiotis, Lambros; Cheung, Sai Hung; Yuen, Ka Veng
Abstract: This paper presents a methodology for general non-linear reliability problems. It is based on dividing the failure domain into a number of appropriately selected subregions and calculating the failure probability as a sum of the probabilities for each subregion. The probability of each subregion is calculated as a product of factors, which can be estimated accurately by a relatively small number of samples generated according to the conditional distribution corresponding to the particular subregion. These samples are generated through Markov Chain Monte Carlo simulations using a slice-sampling-based algorithm proposed by the authors. The proposed method is robust and is suitable for high-dimensional problems. This is in contrast to popular importance sampling methods that often break down for high-dimensional problems. The method is found to be significantly more efficient than Monte Carlo simulations. The efficiency of the method is demonstrated with two examples involving 4000 and 1501 random variables. Copyright © 2010 Inderscience Enterprises Ltd.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/2960682010-01-01T00:00:00Z
- Decoupled reliability-based geotechnical design of deep excavations of soil with spatial variabilityhttp://hdl.handle.net/10722/296207Title: Decoupled reliability-based geotechnical design of deep excavations of soil with spatial variability
Authors: Liu, Wang Sheng; Cheung, Sai Hung
Abstract: © 2020 Elsevier Inc. This paper presents a general decoupled method for reliability-based geotechnical design that takes into account the spatial variability of soil properties. In this method, reliability analyses that require a lot of computational resources are decoupled from the optimization procedure by approximating the failure probability function globally. Failure samples are iteratively generated over the entire design space so that their global distribution information can be extracted to construct the failure probability function. The method is computationally efficient, is flexible to implement, and is well suited for geotechnical problems that may involve sophisticated models. A design example of two-dimensional deep excavation against basal heave is discussed for Singapore marine clay where the density and normalized undrained shear strength of soil mass are modeled as random fields. Results demonstrate that the proposed method works well in practice and is advantageous over the coupled or locally decoupled reliability-based geotechnical design methods.
Wed, 01 Jan 2020 00:00:00 GMThttp://hdl.handle.net/10722/2962072020-01-01T00:00:00Z
- Bayesian uncertainty quantification of recent shock tube determinations of the rate coefficient of reaction H + O<inf>2</inf>→ OH + Ohttp://hdl.handle.net/10722/296250Title: Bayesian uncertainty quantification of recent shock tube determinations of the rate coefficient of reaction H + O<inf>2</inf>→ OH + O
Authors: Miki, Kenji; Cheung, Sai Hung; Prudencio, Ernesto E.; Varghese, Philip L.
Abstract: We analyze the ignition delay in hydrogen-oxygen combustion and the important chain -branching reaction H + O2→ OH + O that occurs behind the shock waves in shock tube experiments. We apply a stochastic Bayesian approach to quantify uncertainties in the theoretical model and experimental data. The approach involves a statistical inverse problem, which has four "components" as input information: (a) model, (b) prior joint probability density function (PDF) of the uncertain parameters, (c) experimental data, and (d) uncertainties in the scenario parameters. The solution of this statistical inverse problem is a posterior joint PDF of the uncertain parameters from which we can easily extract statistical information. We first perform a parametric study to investigate how the level of the total uncertainty (which we define as the sum of model uncertainty and experimental uncertainty) affects the uncertainty in the rate coefficient k1 of the reaction H + O 2→ OH + O, which is "most likely" expressed by k1=1.73×1023T-2.5exp(-11550/T) cm3 mol-1 s-1 over the experimental temperature range 1100-1472 K. We also introduce the idea of "irreducible" uncertainty when considering other parameters in the system. After statistically calibrating the parameters modeling the rate coefficient k1, we predict its 95% confidence interval (CI) for different temperature regimes and compare the CI against the values of k1 obtained deterministically. Our results show that a small uncertainty in gas temperature (±5 K) introduces appreciable uncertainty in k1. © 2012 Wiley Periodicals, Inc.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2962502012-01-01T00:00:00Z
- On the (in)validation of a thermochemical model with EAST shock tube radiation measurementshttp://hdl.handle.net/10722/296067Title: On the (in)validation of a thermochemical model with EAST shock tube radiation measurements
Authors: Miki, Kenji; Panesi, Marco; Prudencio, Ernesto E.; Maurente, Andre; Cheung, Sai Hung; Jagodzinski, Jeremy; Goldstein, David; Prudhomme, Serge; Schulz, Karl; Simmons, Chris; Strand, James; Varghese, Philip
Abstract: The PECOS Center is devoted to the development of a systematic methodology for the (in)validation of physical models under uncertainty. The methodology involves a statistical-based approach for the calibration of uncertain model parameters, the validation of the model itself, and the quantification of uncertainty associated with specific model predictions. It requires the use of experimental data for parameter calibration and model validation, and one ultimatelyappeals to expert opinion for correct interpretation of the results; i.e. a model is considered (in)valid if with the benefit of the above-mentioned statistical insight one, considers it (un)able to provide reliable predictions of specific quantities of interest at specific prediction scenarios. The investigation described in this paper considers the first step of the methodology described above - i.e. uncertain model parameter calibration - as well as the quantification of uncertainty in that calibration. A physicomathematical model used to simulate atomic radiation in shock-heated air plasmas was developed and a stochastic system based Bayesian approach 1, 2 was applied for the quantification of model and model parameter uncertainties. In particular, spectrally and spatially resolved absolute volumetric radiance data collected at the Electric Arc Shock Tube (EAST), located at the NASA Ames Research Center (ARC), were used to simultaneously calibrate a total of twenty-three random parameters by solving a statistical inverse problem. The parameters include a stochastic form of several Arrhenius reaction coefficients, Einstein coefficients, line broadening parameters, and shock tube operational parameters. The results indicate that there is a sufficient amount of experimental data to characterize the value of each model parameter. Obviously, these predicted values are conditional on the physical model and experimental data utilized. After statisitically calibrating the parameters we propagate their uncertainties through the solution of a statistical forward problem where the quantity of interest and the scenario of interest are those specified by the experimental setting. These forward calculations allow us to perform a basic and important verification of our computations.
Fri, 01 Jan 2010 00:00:00 GMThttp://hdl.handle.net/10722/2960672010-01-01T00:00:00Z
- Evaluating the long-term fatigue response of mooring lines using an asymptotic approximationhttp://hdl.handle.net/10722/296084Title: Evaluating the long-term fatigue response of mooring lines using an asymptotic approximation
Authors: Low, Ying Min; Cheung, Sai Hung
Abstract: A major challenge in the design of a mooring line or riser is the evaluation of the fatigue damage accumulated over the lifetime of the structure. The long-term environmental condition is usually represented by a scatter diagram, or a joint probability density of the significant waveheight and a characteristic wave period. Since it is computationally impractical to consider a large number of sea states, a common practice is to condense the sea states into a small number of blocks, but this procedure inevitably introduces significant errors owing to the coarse discretization. In view of the need for efficient but accurate approaches, this paper investigates the application of an asymptotic approximation, which is an established technique for estimating probability integrals, but it has so far not been applied to the fatigue design of moorings and risers. In addition, a classical method known as the perturbation approach is examined. The above approximate methods are implemented on an FPSO floating system, and direct numerical integration is carried out to ascertain the accuracy of the approximate solutions. Copyright © 2012 by ASME.
Sun, 01 Jan 2012 00:00:00 GMThttp://hdl.handle.net/10722/2960842012-01-01T00:00:00Z
- Long-term fatigue analysis of risers with multiple environmental random variables in time domainhttp://hdl.handle.net/10722/296113Title: Long-term fatigue analysis of risers with multiple environmental random variables in time domain
Authors: Gao, Yidan; Cheung, Sai Hung
Abstract: Copyright © 2015 by the International Society of Offshore and Polar Engineers (ISOPE). The deep-water offshore engineering industry is developed greatly with the increasing demand for oil and gas. Floating structural systems are usually exposed to many types of environmental loads like wave, wind, current and so on. There are two common types of limit state criteria for consideration in the dynamic analysis, which are fatigue limit state and ultimate limit state. For ultimate limit state, extreme response prediction would be assessed by either short-term or long-term method. For the fatigue analysis of riser and mooring systems, a long-term analysis in time domain is considered as the most accurate fatigue assessment approaches. However, in the long-term analysis, all possible sea states should be considered which is rather computationally time-consuming to simulate numerous sea states by classical numerical or Monte Carlo method, especially when more environmental random variables are taken into account. The conventional way for a long-term fatigue calculation recommended by design codes is the practice of lumping of sea states into a small number of manageable bins. However, there are no explicit guidelines for blocking strategy and the precision of this method cannot be guaranteed. There are some researchers studying this long-term fatigue problem, but considering only two environmental random variables: significant wave height and spectrum peak period. However, there appear to be no precedent studies for the long-term fatigue analysis of mooring and riser systems considering more than two environmental random variables. Hence, a proposed simulation approach based on State-of-the-art advanced response surface method is explored in this paper to assess the long term fatigue damage of flexible risers in deep water condition considering five environmental random variables: significant wave height Hs, spectral peak period Tp, mean wind velocity Vw, mean current velocity at the sea surface Vc and wave direction θ. The results show reasonable accuracy compared with numerical method with the using of response surface method, but with much higher computational efficiency.
Thu, 01 Jan 2015 00:00:00 GMThttp://hdl.handle.net/10722/2961132015-01-01T00:00:00Z
- Algorithms for Bayesian model class selection of higher-dimensional dynamic systemshttp://hdl.handle.net/10722/296043Title: Algorithms for Bayesian model class selection of higher-dimensional dynamic systems
Authors: Cheung, Sai Hung; Beck, James L.
Abstract: In recent years, Bayesian model updating techniques based on measured data have been applied in structural health monitoring. Often we are faced with the problem of how to select the 'best' model from a set of competing candidate model classes for the system based on data. To tackle this problem, Bayesian model class selection is used, which provides a rigorous Bayesian updating procedure to give the probability of different candidate classes for a system, based on the data from the system. There may be cases where more than one model class has significant probability and each of these will give different predictions. Bayesian model class averaging provides a coherent mechanism to incorporate all the considered model classes in the probabilistic predictions for the system. However, both Bayesian model class selection and Bayesian model class averaging require the calculation of the evidence of the model class which requires the nontrivial computation of a multidimensional integral. In this paper, several methods for solving this computationally challenging problem of model class selection are presented, proposed and compared. The efficiency of the proposed methods is illustrated by an example involving a structural dynamic system. Copyright © 2007 by ASME.
Tue, 01 Jan 2008 00:00:00 GMThttp://hdl.handle.net/10722/2960432008-01-01T00:00:00Z
- On the use of Bayesian approach, experimental data, and artificially designed data, for the identification of missing reactionshttp://hdl.handle.net/10722/296072Title: On the use of Bayesian approach, experimental data, and artificially designed data, for the identification of missing reactions
Authors: Miki, Kenji; Cheung, Sai Hung; Prudencio, Ernesto E.
Abstract: We propose and analyze the use of a Bayesian approach for the investigation of missing reactions, considering both modeling and experimental uncertainties. The main idea is the use of two calibration data sets: one is experimental and the other is artificially designed in such a way that features observed in the experimental data set are magnified. We apply our proposed methodology on the investigation of a combustion kinetics phenomena that is modeled with a highly nonlinear system of several ordinary differential equations. More specifically, we quantify uncertainties in the reduced chemistry (6 reactions) model of the HCN/O2/Ar mixture kinetics proposed by Thielen and Roth,1 and try to identify which reaction(s) should be added to the reduced chemistry in order to improve its predictions of species concentration profiles at high temperature scenarios. Our data analysis methodology using both experimental and artificially designed data indeed helps us identify some critical reactions out of the pool of 27 extra reactions. Due to its simplicity, the approach can be potentially applied to a wide variety of engineering problems. Copyright © 2011 by Kenji Miki.
Sat, 01 Jan 2011 00:00:00 GMThttp://hdl.handle.net/10722/2960722011-01-01T00:00:00Z
- Bayesian updating of failure probability curves with multiple performance functions of nonlinear structural dynamic systemshttp://hdl.handle.net/10722/319075Title: Bayesian updating of failure probability curves with multiple performance functions of nonlinear structural dynamic systems
Authors: Hao, C; Cheung, SH
Sat, 01 Jan 2022 00:00:00 GMThttp://hdl.handle.net/10722/3190752022-01-01T00:00:00Z
- On the Bayesian sensor placement for two-stage structural model updating and its validationhttp://hdl.handle.net/10722/319594Title: On the Bayesian sensor placement for two-stage structural model updating and its validation
Authors: Bansal, S; Cheung, SH
Sat, 01 Jan 2022 00:00:00 GMThttp://hdl.handle.net/10722/3195942022-01-01T00:00:00Z