The Bayesian Compromise Decision Support Problem for Multilevel Design Involving Uncertainty

1994 ◽  
Vol 116 (2) ◽  
pp. 388-395 ◽  
Author(s):  
S. Vadde ◽  
J. K. Allen ◽  
F. Mistree

In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian statistics into the traditional compromise DSP formulation and describe some of the pending research issues.

Author(s):  
S. Vadde ◽  
R. S. Krishnamachari ◽  
F. Mistree ◽  
J. K. Allen

Abstract In this paper we present an extension to the traditional compromise Decision Support Problem (DSP) formulation. In this formulation we use Bayesian Statistics to model uncertainties associated with the information being used. In an earlier paper we have introduced a compromise DSP that accounts for uncertainty using fuzzy set theory. In this paper we describe the Bayesian Decision Support Problem. We use this formulation to design a portal frame structure. We discuss the results and compare them with those obtained using the Fuzzy DSP. Finally, we discuss the efficacy of incorporating Bayesian Statistics into the traditional compromise DSP formulation and describe some of the pending research issues.


Author(s):  
S. Vadde ◽  
S. Swadi ◽  
N. Bhattacharya ◽  
F. Mistree ◽  
J. K. Allen

Abstract During the early stages of project initiation, the information available to a designer may be uncertain (imprecise or stochastic). In response to this need, two extensions of the crisp compromise Decision Support Problem using fuzzy set theory and Bayesian statistics are developed to model uncertainty in design problems. The fuzzy compromise DSP is used to model imprecise information and the Bayesian compromise DSP is used to model stochastic information. The design of an aircraft tire is used as an illustrative example.


1967 ◽  
Vol 38 (1) ◽  
pp. 143-146
Author(s):  
R SHEPHERD ◽  
W M EL DAKHAKHNI ◽  
E R BRYAN

2011 ◽  
Vol 71-78 ◽  
pp. 3605-3609
Author(s):  
De Zhi Liang ◽  
Min Huang

In recent years, as the portal frame’s height toward higher and the span toward wider, the influence of wind vibration becomes more and more prominent among the portal frame structure. In the design of the portal frame, there are many different opinions on whether considering the impact of the vertical wind vibration to the portal frame. This paper taking a true engineering as an example, using finite element software to establish the solid model of the portal frame structure, selecting the junction of purlin and roof as a node of imposing vertical fluctuating wind load, we made numerical simulation analysis of vertical wind vibration. The simulation results will be compared with data of the internal forces and deformation under the average wind pressure. The results showed that: vertical wind vibration has a prominent effect to the portal frame and should be considered in the design.


2017 ◽  
Vol 24 (16) ◽  
pp. 3684-3697 ◽  
Author(s):  
Rodrigo T Rocha ◽  
Jose M Balthazar ◽  
Angelo M Tusset ◽  
Vinicius Piccirillo

This work presents a passive control strategy using a pendulum on a simple portal frame structure, with two-to-one internal resonance, with a piezoelectric material coupling as a means of energy harvesting. In addition, the system is externally base-excited by an electro-dynamical shaker with harmonic output. Due to internal resonance the system may present the phenomenon of saturation, which provides some nonlinear dynamical behavior to the system. A pendulum is coupled to control nonlinear behaviors, leading to a periodic orbit, which is necessary to maintain energy harvesting. The results show that the system presents, most of the time, as being quasiperiodic. However, it does not present as being chaotic. With the pendulum, it was possible to control most of these quasiperiodic behaviors, leading to a periodic orbit. Moreover, it is possible to eliminate the need for an active or semi-active control, which are usually more complex. In addition, the control provides a way to detune the energy captured to the desired operating frequency.


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