Von Kármán equations with uncertain input data and the worst scenario method

2007 ◽  
Vol 87 (10) ◽  
pp. 747-761
Author(s):  
I. Hlaváček
Keyword(s):  
Input Data ◽  
Von Karman ◽  
Von Kármán Equations ◽  
Scenario Method ◽  
Uncertain Input ◽  
Uncertain Input Data ◽  
Von Kármán

scholarly journals A Review on Analytical Modeling for Collapse Mode Capacitive Micromachined Ultrasonic Transducer of the Collapse Voltage and the Static Membrane Deflections

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 714
Author(s):  
Jiujiang Wang ◽  
Xin Liu ◽  
Yuanyu Yu ◽  
Yao Li ◽  
Ching-Hsiang Cheng ◽  
...  
Keyword(s):  
Analytical Modeling ◽  
Ultrasonic Transducer ◽  
Governing Equations ◽  
Von Karman ◽  
Von Kármán Equations ◽  
Collapse Mode ◽  
Capacitive Micromachined Ultrasonic Transducer ◽  
Gap Height ◽  
Von Kármán ◽  
Analytical Expressions

Analytical modeling of capacitive micromachined ultrasonic transducer (CMUT) is one of the commonly used modeling methods and has the advantages of intuitive understanding of the physics of CMUTs and convergent when modeling of collapse mode CMUT. This review article summarizes analytical modeling of the collapse voltage and shows that the collapse voltage of a CMUT correlates with the effective gap height and the electrode area. There are analytical expressions for the collapse voltage. Modeling of the membrane deflections are characterized by governing equations from Timoshenko, von Kármán equations and the 2D plate equation, and solved by various methods such as Galerkin’s method and perturbation method. Analytical expressions from Timoshenko’s equation can be used for small deflections, while analytical expression from von Kármán equations can be used for both small and large deflections.

A Fuzzy Approach to Disaster Modeling

2013 ◽  
pp. 1038-1055
Author(s):  
Jan Stoklasa
Keyword(s):  
Disaster Response ◽  
Initial Phase ◽  
Input Data ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Linguistic Variables ◽  
Rescue Services ◽  
Uncertain Input ◽  
Uncertain Input Data ◽  
Rule Bases

The decision making process of the Emergency Medical Rescue Services (EMRS) operations centre during disasters involves a significant amount of uncertainty. Decisions need to be made quickly, and no mistakes are tolerable, particularly in the case of disasters resulting in a large number of injured people. A multiphase linguistic fuzzy model is introduced to assist the operator during the initial phase of the medical disaster response. Based on uncertain input data, estimating the severity of the disaster, the number of injured people, and the amount of forces and resources needed to successfully deal with the situation is possible. The need for reinforcements is also considered. Fuzzy numbers, linguistic variables and fuzzy rule bases are applied to deal with the uncertainty. Outputs provided by the model (severity of the disaster, number of reinforcements needed etc.) are available both as fuzzy sets (for the purposes of disaster planning) and linguistic terms (for emergency call evaluation purposes).

Sign in / Sign up

Export Citation Format

Share Document