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1 – 10 of over 40000Discusses the methods of sensitivity analysis in use generally andby the property appraisal profession. Proposes a simplified structuredand systematic technique of selecting…
Abstract
Discusses the methods of sensitivity analysis in use generally and by the property appraisal profession. Proposes a simplified structured and systematic technique of selecting critical or sensitive factors for sensitivity analysis in property development and investment appraisal. Concludes that sensitivity analysis has become an integral part of property appraisal.
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Yuxin Cui, Yong-Hua Li, Dongxu Zhang, Yufeng Wang and Zhiyang Zhang
Aiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.
Abstract
Purpose
Aiming at the inefficiency of solving the Sobol index using the traditional mathematical analytical method, a Sobol global sensitivity analysis method is proposed.
Design/methodology/approach
In this paper, a support vector regression (SVR) surrogate model is constructed to solve the Sobol index. The optimal combination of SVR hyperparameters is obtained by using the improved beluga whale optimization (IBWO). Meanwhile, in order to solve the problem that Sobol sequences will form correlation regions in high-dimensional space leading to the uneven distribution of sampling points, a scrambled strategy is introduced in the Sobol sensitivity analysis using IBWO-SVR. Thus, the IBWO-SVR-SS sensitivity analysis model is established.
Findings
The results of two test functions show that the method further improves the accuracy of the sensitivity analysis. Finally, the first-order Sobol index and second-order Sobol index are solved by the IBWO-SVR-SS method using the metro bogie frame as an engineering example. Through the analysis results, the key design parameters of the frame and the design parameter combinations with more obvious coupling relationships are identified, providing a strong reference for the subsequent analysis and structural optimization.
Originality/value
Sobol sensitivity analysis using the surrogate model method can effectively improve the efficiency of the solution. In addition, IBWO is used for the optimization of the SVR hyperparameters to improve the accuracy and efficiency of the optimization, and finally, the correction of the Sobol sequence through the introduction of the disruption strategy also further improves the accuracy of the sensitivity analysis of Sobol.
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Xin Li, Jianzhong Shang and Hong Zhu
This paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins…
Abstract
Purpose
This paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research.
Design/methodology/approach
To date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change.
Findings
Following the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given assembly process parameters.
Originality/value
A case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.
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B.J. Henz, K.K. Tamma, R. Kanapady, N.D. Ngo and P.W. Chung
The resin transfer molding process for composites manufacturing consists of either of two considerations, namely, the fluid flow analysis through a porous fiber preform where the…
Abstract
The resin transfer molding process for composites manufacturing consists of either of two considerations, namely, the fluid flow analysis through a porous fiber preform where the location of the flow front is of fundamental importance, and the combined flow/heat transfer/cure analysis. In this paper, the continuous sensitivity formulations are developed for the process modeling of composites manufactured by RTM to predict, analyze, and optimize the manufacturing process. Attention is focused here on developments for isothermal flow simulations, and various illustrative examples are presented for sensitivity analysis of practical applications which help serve as a design tool in the process modeling stages.
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Yidu Zhang, Yongshou Liu and Qing Guo
This paper aims to develop a method for evaluating the failure probability and global sensitivity of multiple failure modes based on convex-probability hybrid uncertainty.
Abstract
Purpose
This paper aims to develop a method for evaluating the failure probability and global sensitivity of multiple failure modes based on convex-probability hybrid uncertainty.
Design/methodology/approach
The uncertainty information of the input variable is considered as convex-probability hybrid uncertainty. Moment-independent variable global sensitivity index based on the system failure probability is proposed to quantify the effect of the input variable on the system failure probability. Two-mode sensitivity indices are adopted to characterize the effect of each failure mode on the system failure probability. The method based on active learning Kriging (ALK) model with a truncated candidate regions (TCR) is adopted to evaluate the systems failure probability, as well as sensitivity index and this method is termed as ALK-TCR.
Findings
The results of five examples demonstrate the effectiveness of the sensitivity index and the efficiency of the ALK-TCR method in solving the problem of multiple failure modes based on the convex-probability hybrid uncertainty.
Originality/value
Convex-probability hybrid uncertainty is considered on system reliability analysis. Moment-independent variable sensitivity index based on the system failure probability is proposed. Mode sensitivity indices are extended to hybrid uncertain reliability model. An effective global sensitivity analysis approach is developed for the multiple failure modes based on convex-probability hybrid uncertainty.
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Zaihua Luo, Juliang Xiao, Sijiang Liu, Mingli Wang, Wei Zhao and Haitao Liu
This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too…
Abstract
Purpose
This paper aims to propose a dynamic parameter identification method based on sensitivity analysis for the 5-degree of freedom (DOF) hybrid robots, to solve the problems of too many identification parameters, complex model, difficult convergence of optimization algorithms and easy-to-fall into a locally optimal solution, and improve the efficiency and accuracy of dynamic parameter identification.
Design/methodology/approach
First, the dynamic parameter identification model of the 5-DOF hybrid robot was established based on the principle of virtual work. Then, the sensitivity of the parameters to be identified is analyzed by Sobol’s sensitivity method and verified by simulation. Finally, an identification strategy based on sensitivity analysis was designed, experiments were carried out on the real robot and the results were verified.
Findings
Compared with the traditional full-parameter identification method, the dynamic parameter identification method based on sensitivity analysis proposed in this paper converges faster when optimized using the genetic algorithm, and the identified dynamic model has higher prediction accuracy for joint drive forces and torques than the full-parameter identification models.
Originality/value
This work analyzes the sensitivity of the parameters to be identified in the dynamic parameter identification model for the first time. Then a parameter identification method is proposed based on the results of the sensitivity analysis, which can effectively reduce the parameters to be identified, simplify the identification model, accelerate the convergence of the optimization algorithm and improve the prediction accuracy of the identified model for the joint driving forces and torques.
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Wakae Kozukue and Ichiro Hagiwara
One of the authors has already formulated the sensitivity analysis for a coupled structural‐acoustic system and applied the method in order to obtain modal sensitivities and modal…
Abstract
One of the authors has already formulated the sensitivity analysis for a coupled structural‐acoustic system and applied the method in order to obtain modal sensitivities and modal frequency response sensitivities for the sound pressure level at peak frequency points. However, for the development of a vehicle, not only the reduction of peak frequency level but also that of integral of noise for a specified frequency range is desired. For investigating this it is considered effective to use sensitivities of integrated sound pressure level for a specified frequency range. Thus a “sound pressure level integral” has been developed, which is the integrated value of sound pressure level, and further “sensitivity of sound pressure level integral”. Shows how an integral analysis process is performed, and how vibration and noise can be reduced.
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This paper aims to present a new method, named as augmented polynomial dimensional decomposition (PDD) method, for robust design optimization (RDO) and reliability-based design…
Abstract
Purpose
This paper aims to present a new method, named as augmented polynomial dimensional decomposition (PDD) method, for robust design optimization (RDO) and reliability-based design optimization (RBDO) subject to mixed design variables comprising both distributional and structural design variables.
Design/methodology/approach
The method involves a new augmented PDD of a high-dimensional stochastic response for statistical moments and reliability analyses; an integration of the augmented PDD, score functions, and finite-difference approximation for calculating the sensitivities of the first two moments and the failure probability with respect to distributional and structural design variables; and standard gradient-based optimization algorithms.
Findings
New closed-form formulae are presented for the design sensitivities of moments that are simultaneously determined along with the moments. A finite-difference approximation integrated with the embedded Monte Carlo simulation of the augmented PDD is put forward for design sensitivities of the failure probability.
Originality/value
In conjunction with the multi-point, single-step design process, the new method provides an efficient means to solve a general stochastic design problem entailing mixed design variables with a large design space. Numerical results, including a three-hole bracket design, indicate that the proposed methods provide accurate and computationally efficient sensitivity estimates and optimal solutions for RDO and RBDO problems.
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Raghavendra Rao N.S. and Chitra A.
The purpose of this study is to extend a sensitivity-based reliability technique for the processors deployed in industrial drive (ID).
Abstract
Purpose
The purpose of this study is to extend a sensitivity-based reliability technique for the processors deployed in industrial drive (ID).
Design/methodology/approach
The processor provides flexible operation, re-configurability, and adaptable compatibility in industrial motor drive system. A sensitivity-based model allows a robust tool for validating the system design. Sensitivity is the probability of a partial failure rate for a distributed variable; sensitivity and failure rates are also complementary. Conversely, traditional power electronic components reliability estimating standards have overlooked it, and it is essential to update them to account for the sensitivity parameter. A new sensitivity-based reliability prediction methodology for a typical 32-bit microprocessor operating at 30ºC deployed in ID is presented to fill this gap. The proposed techniques are compared with the estimated processor reliability values obtained from various reliability standards using the validated advanced logistics development tool. The main contribution of this work is to provide a sensitivity extended reliability method over the conventional method directing toward improving reliability, availability, and maintainability in the design of ID.
Findings
The analysis shows that the sensitivity of the processor’s circuit increases due to increases in complexity of the system by reducing the overall mean time between failure upon comparing among conventional reliability standards.
Originality/value
The significance of this paper lies in the overall, sensitivity-based reliability technique for processors in comparison to the traditional reliability complexity in IDs.
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Stefan Schwarz and Ekkehard Ramm
The present contribution deals with the sensitivity analysis and optimization of structures for path‐dependent structural response. Geometrically as well as materially non‐linear…
Abstract
The present contribution deals with the sensitivity analysis and optimization of structures for path‐dependent structural response. Geometrically as well as materially non‐linear behavior with hardening and softening is taken into account. Prandtl‐Reuss‐plasticity is adopted so that not only the state variables but also their sensitivities are path‐dependent. Because of this the variational direct approach is preferred for the sensitivity analysis. For accuracy reasons the sensitivity analysis has to be consistent with the analysis method evaluating the structural response. The proposed sensitivity analysis as well as its application in structural optimization is demonstrated by several examples.
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