scholarly journals Monte-Carlo simulation for moderate seismic zones using artificial accelerograms

2022 ◽  
Author(s):  
ILIAS BOUNSIR
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Seismic Waves ◽  
Seismic Wave Propagation ◽  
Technical Note ◽  
Explicit Scheme ◽  
Statistical Characteristics ◽  
Seismic Zones ◽  
Artificial Accelerograms

Abstract The present technical note deals with a new method to generate seismic accelerograms based on statistical characteristics and the classification of seismic waves. A short heterogeneous sitebased analyse of these accelerograms is given with a seismic wave propagation code in 1D. After, a numerical simulation is done in order to obtain PGA using Newmark’s explicit scheme, and finally, Monte-Carlo simulation is used to determinate failure’s probability of structures localised in moderate seismic zones in hexagonal France.

DEA and Monte Carlo simulation approach towards green car selection

2017 ◽  
Vol 24 (5) ◽  
pp. 1234-1252 ◽  
Author(s):  
Anand Prakash ◽  
Rajendra P. Mohanty
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Secondary Data ◽  
Research Centre ◽  
Ride Quality ◽  
Data Envelopment ◽  
Distribution Fitting ◽  
Content Type ◽  
Statistical Inferences

Purpose Automakers are engaged in manufacturing both efficient and inefficient green cars. The purpose of this paper is to categorize efficient green cars and inefficient green cars followed by improving efficiencies of identified inefficient green cars for distribution fitting. Design/methodology/approach The authors have used 2014 edition of secondary data published by the Automotive Research Centre of the Automobile Club of Southern California. The paper provides the methodology of applying data envelopment analysis (DEA) consisting of 50 decision-making units (DMUs) of green cars with six input indices (emission, braking, ride quality, acceleration, turning circle, and luggage capacity) and two output indices (miles per gallon and torque) integrated with Monte Carlo simulation for drawing significant statistical inferences graphically. Findings The findings of this study showed that there are 27 efficient and 23 inefficient DMUs along with improvement matrix. Additionally, the study highlighted the best distribution fitting of improved efficient green cars for respective indices. Research limitations/implications This study suffers from limitations associated with 2014 edition of secondary data used in this research. Practical implications This study may be useful for motorists with efficient listing of green cars, whereas automakers can be benefitted with distribution fitting of improved efficient green cars using Monte Carlo simulation for calibration. Originality/value The paper uses DEA to empirically examine classification of green cars and applies Monte Carlo simulation for distribution fitting to improved efficient green cars to decide appropriate range of their attributes for calibration.

scholarly journals Technical Note—The Monte Carlo Simulation of Operating-Room and Recovery-Room Usage

1974 ◽  
Vol 22 (2) ◽  
pp. 434-440 ◽  
Author(s):  
Paul J. Kuzdrall ◽  
N. K. Kwak ◽  
Homer H. Schmitz
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Operating Room ◽  
Recovery Room ◽  
Technical Note

scholarly journals Analytical Solutions for Stochastic Vibration of Orthotropic Membrane under Random Impact Load

Materials ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1231 ◽  
Author(s):  
Dong Li ◽  
Zhou-Lian Zheng ◽  
Rui Yang ◽  
Peng Zhang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Geometric Nonlinearity ◽  
Impact Load ◽  
Heavy Rain ◽  
Statistical Characteristics ◽  
Stochastic Dynamic ◽  
Experiment Study ◽  
Stochastic Vibration ◽  
Force Velocity

Orthotropic membrane materials have been applied in the numerous fields, such as civil engineering, space and aeronautics, and mechanical engineering, among others. During their serving lifespan, these membranes are always facing strong stochastic vibrations induced by the random impact load such as hail, heavy rain, and noise, among others. In this paper, the stochastic vibration problem of orthotropic membrane subjected to random impact load is investigated. The statistical characteristics of random impact load are initially obtained based on the stochastic pulse theory. Then, the Von Karman theory is applied to model the nonlinear vibration of membrane with geometric nonlinearity, which is then used to derive and solve the corresponding fokker–plank–kolmogorov (FPK). The theoretical model developed is validated by means of experiment study and monte carlo simulation (MCS) analysis. The effects of variables like pretension force, velocity of impact load, and material features on stochastic dynamic behavior of membranes are discussed in detail. This exposition provides theoretical framework for stochastic vibration control and design of membranes subjected to random dynamic load.

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