A General Methodology for Uncertainty Quantification in Engineering Analyses Using a Credible Probability Box

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Mark E. Ewing ◽  
Brian C. Liechty ◽  
David L. Black
Keyword(s):  
Uncertainty Quantification ◽  
Probability Distributions ◽  
System Response ◽  
Engineering Analysis ◽  
Safety Factors ◽  
Dimensional Approach ◽  
Analysis And Design ◽  
Sources Of Uncertainty ◽  
Balanced Designs ◽  
Detailed Assessment

Uncertainty quantification (UQ) is gaining in maturity and importance in engineering analysis. While historical engineering analysis and design methods have relied heavily on safety factors (SF) with built-in conservatism, modern approaches require detailed assessment of reliability to provide optimized and balanced designs. This paper presents methodologies that support the transition toward this type of approach. Fundamental concepts are described for UQ in general engineering analysis. These include consideration of the sources of uncertainty and their categorization. Of particular importance are the categorization of aleatory and epistemic uncertainties and their separate propagation through an UQ analysis. This familiar concept is referred to here as a “two-dimensional” approach, and it provides for the assessment of both the probability of a predicted occurrence and the credibility in that prediction. Unique to the approach presented here is the adaptation of the concept of a bounding probability box to that of a credible probability box. This requires estimates for probability distributions related to all uncertainties both aleatory and epistemic. The propagation of these distributions through the uncertainty analysis provides for the assessment of probability related to the system response, along with a quantification of credibility in that prediction. Details of a generalized methodology for UQ in this framework are presented, and approaches for interpreting results are described. Illustrative examples are presented.

scholarly journals Parametric optimization of the cross-section and shape of the longitudinal member in frontal impact

2019 ◽  
Vol 140 ◽  
pp. 02013
Author(s):  
Dmitry Bogdanov ◽  
Yury Boldyrev ◽  
Pavel Cvetkov ◽  
Oleg Klyavin ◽  
Ilya Davydov ◽  
...  
Keyword(s):  
Cross Section ◽  
Parametric Optimization ◽  
Numerical Procedure ◽  
Software Systems ◽  
Engineering Analysis ◽  
Frontal Impact ◽  
Mathematical Statement ◽  
Analysis And Design ◽  
Optimization Task ◽  
Python Programming

The article considers the problem of optimal design of car body elements (longitudinal members) according to the chosen criteria. Both the questions of formulation of the optimization task and individual problems of its solution are studied. The mathematical statement of the problem is considered. Thus, the most attention is given to consideration of realisation of used numerical procedure of optimization. The system of numerical calculations is based on the most widely spread software systems for engineering analysis and design. The developed scripts on Python programming language are briefly considered. Results of optimization of longitudinal members of the car are given.

scholarly journals Machine Learning and Cryptographic Algorithms – Analysis and Design in Ransomware and Vulnerabilities Detection

2020 ◽  
Author(s):  
Nandkumar Niture
Keyword(s):  
Machine Learning ◽  
Intelligent System ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
System Level ◽  
Management Systems ◽  
System Response ◽  
Hard Part ◽  
Analysis And Design ◽  
Password Management

The AI, deep learning and machine learning algorithms are gaining the ground in every application domain of information technology including information security. In formation security domain knows for traditional password management systems, auto-provisioning systems and user information management systems. There is another raising concern on the application and system level security with ransomware. On the existing systems cyber-attacks of Ransomware asking for ransom increasing every day. Ransomware is the class of malware where the goal is to gain the data through encryption mechanism and render back with the ransom. The ransomware attacks are mainly on the vulnerable systems which are exposed to the network with weak security measures. With the help of machine learning algorithms, the pattern of the attacks can be analyzed. Create or discuss a workaround solution of a machine learning model with combination of cryptographic algorithm which will enhance the effectiveness of the system response to the possible attacks. The other part of the problem, which is hard part to create an intelligence for the organizations for preventing the ransomware attacks with the help of intelligent system password management and intelligent account provisioning. In this paper I elaborate on the machine learning algorithms analysis for the intelligent ransomware detection problem, later part of this paper would be design of the algorithm.

Possibilistic Uncertainty Quantification in 1-D Consolidation Problems

2021 ◽  
Author(s):  
Djamalddine Boumezerane
Keyword(s):  
Uncertainty Quantification ◽  
Probability Distributions ◽  
Uncertainty Propagation ◽  
Possibility Distribution ◽  
Coefficient Of Consolidation ◽  
Consolidation Process ◽  
Possibility Distributions ◽  
Coverage Function ◽  
The One ◽  
Measure Of Uncertainty

Abstract In this study, we use possibility distribution as a basis for parameter uncertainty quantification in one-dimensional consolidation problems. A Possibility distribution is the one-point coverage function of a random set and viewed as containing both partial ignorance and uncertainty. Vagueness and scarcity of information needed for characterizing the coefficient of consolidation in clay can be handled using possibility distributions. Possibility distributions can be constructed from existing data, or based on transformation of probability distributions. An attempt is made to set a systematic approach for estimating uncertainty propagation during the consolidation process. The measure of uncertainty is based on Klir's definition (1995). We make comparisons with results obtained from other approaches (probabilistic…) and discuss the importance of using possibility distributions in this type of problems.

A Review of Mooring Analysis Methodologies for Permanent Offshore Installations

2020 ◽  
Author(s):  
Spiro J. Pahos ◽  
Georgina Maldonado ◽  
Paul C. Westlake
Keyword(s):  
Time Domain ◽  
Line Strength ◽  
Time Domain Analysis ◽  
Domain Analysis ◽  
Mooring Line ◽  
System Response ◽  
Mooring System ◽  
Deterministic Approach ◽  
Safety Factors ◽  
Analytical Approaches

Abstract Traditionally mooring line strength assessment is based on a deterministic approach, where the mooring system is evaluated for a design environment defined by a return period. The mooring system response is then checked against the mooring strength to ensure a required factor of safety. Some codes adopt a deterministic approach [1], [2], [3]. Other codes like [4] adopt a partial safety factor format where uncertainties are addressed through load factors for load components and material factors for line strength. Industry practices give guidance on mooring analysis methodology together with analysis options like coupled, de-coupled, time domain, frequency domain and the associated line tension safety factors. Prior work has demonstrated that discrepancies in mooring line tensions are observed when different analytical approaches are used [5]. Namely, the mooring line tensions of a semi-submersible unit in a coupled time domain analysis, were found to be non-compliant, whereas those calculated using a decoupled time domain analysis returned compliant tensions. This work focuses on a coupled dynamic analysis where all inertial, hydrodynamic and mechanical forces are assessed to determine the subsequent motions. Despite being considered the most accurate to capture the true dynamic response, a coupled analysis is also the least efficient in terms of the required computer resources and engineering effort [1]. This paper presents further discussion on the above observation in mooring tensions and also considers differences in the installation’s excursion. All responses are evaluated in the time domain where the nonlinear dynamic behavior of the mooring lines, slowly varying wave drift forces and coupling effects are captured. Agreement is found in the present computations, carried out with two renowned hydrodynamic codes, which validate former results and reiterate the need to distinguish between time domain methods and recommended appropriate safety factors accordingly.

Random Field Characterization Considering Statistical Dependence for Probability Analysis and Design

2010 ◽  
Vol 132 (10) ◽  
Author(s):  
Zhimin Xi ◽  
Byeng D. Youn ◽  
Chao Hu
Keyword(s):  
Random Field ◽  
Goodness Of Fit ◽  
Probability Analysis ◽  
Statistical Dependence ◽  
System Response ◽  
Goodness Of Fit Test ◽  
Optimal Sequence ◽  
Engineering Product ◽  
Analysis And Design ◽  
Rosenblatt Transformation

The proper orthogonal decomposition method has been employed to extract the important field signatures of random field observed in an engineering product or process. Our preliminary study found that the coefficients of the signatures are statistically uncorrelated but may be dependent. To this point, the statistical dependence of the coefficients has been ignored in the random field characterization for probability analysis and design. This paper thus proposes an effective random field characterization method that can account for the statistical dependence among the coefficients for probability analysis and design. The proposed approach has two technical contributions. The first contribution is the development of a natural approximation scheme of random field while preserving prescribed approximation accuracy. The coefficients of the signatures can be modeled as random field variables, and their statistical properties are identified using the chi-square goodness-of-fit test. Then, as the paper’s second technical contribution, the Rosenblatt transformation is employed to transform the statistically dependent random field variables into statistically independent random field variables. The number of the transformation sequences exponentially increases as the number of random field variables becomes large. It was found that improper selection of a transformation sequence among many may introduce high nonlinearity into system responses, which may result in inaccuracy in probability analysis and design. Hence, this paper proposes a novel procedure of determining an optimal sequence of the Rosenblatt transformation that introduces the least degree of nonlinearity into the system response. The proposed random field characterization can be integrated with any advanced probability analysis method, such as the eigenvector dimension reduction method or polynomial chaos expansion method. Three structural examples, including a microelectromechanical system bistable mechanism, are used to demonstrate the effectiveness of the proposed approach. The results show that the statistical dependence in the random field characterization cannot be neglected during probability analysis and design. Moreover, it is shown that the proposed random field approach is very accurate and efficient.

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