scholarly journals Large system decomposition and simulation methodology using axiomatic analysis

2010 ◽  
Author(s):  
L Spenner ◽  
P Krier ◽  
M Thornton ◽  
S Nair ◽  
S Szygenda ◽  
...  
Keyword(s):  
Large System ◽  
System Decomposition ◽  
Simulation Methodology ◽  
Axiomatic Analysis

Simulation Methodology

1991 ◽  
Author(s):  
Peter W. Glynn ◽  
Donald L. Iglehart
Keyword(s):  
Simulation Methodology

scholarly journals Delay-Dependent Robust Stabilization for Nonlinear Large Systems via Decentralized Fuzzy Control

2011 ◽  
Vol 2011 ◽  
pp. 1-20 ◽  
Author(s):  
Chun-xia Dou ◽  
Zhi-sheng Duan ◽  
Xing-bei Jia ◽  
Xiao-gang Li ◽  
Jin-zhao Yang ◽  
...  
Keyword(s):  
Time Delay ◽  
Fuzzy Control ◽  
Upper Bound ◽  
Time Delays ◽  
Sufficient Conditions ◽  
Large System ◽  
Disturbance Attenuation ◽  
Robust Controller ◽  
Large Systems ◽  
Delay Dependent

A delay-dependent robust fuzzy control approach is developed for a class of nonlinear uncertain interconnected time delay large systems in this paper. First, an equivalent T–S fuzzy model is extended in order to accurately represent nonlinear dynamics of the large system. Then, a decentralized state feedback robust controller is proposed to guarantee system stabilization with a prescribedH∞disturbance attenuation level. Furthermore, taking into account the time delays in large system, based on a less conservative delay-dependent Lyapunov function approach combining with linear matrix inequalities (LMI) technique, some sufficient conditions for the existence ofH∞robust controller are presented in terms of LMI dependent on the upper bound of time delays. The upper bound of time-delay and minimizedH∞performance index can be obtained by using convex optimization such that the system can be stabilized and for all time delays whose sizes are not larger than the bound. Finally, the effectiveness of the proposed controller is demonstrated through simulation example.

scholarly journals Monte Carlo Simulation Methodology to Assess the Impact of Ambient Wind on Emissions from a Light-Commercial Vehicle Running on the Worldwide-Harmonized Light-Duty Vehicles Test Cycle (WLTC)

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 661
Author(s):  
Alexandros T. Zachiotis ◽  
Evangelos G. Giakoumis
Keyword(s):  
Monte Carlo Simulation ◽  
Energy Consumption ◽  
Commercial Vehicle ◽  
Ambient Wind ◽  
Simulation Methodology ◽  
Road Load ◽  
Light Duty ◽  
Light Commercial Vehicle ◽  
Light Duty Vehicles ◽  
The Impact

A Monte Carlo simulation methodology is suggested in order to assess the impact of ambient wind on a vehicle’s performance and emissions. A large number of random wind profiles is generated by implementing the Weibull and uniform statistical distributions for wind speed and direction, respectively. Wind speed data are drawn from eight cities across Europe. The vehicle considered is a diesel-powered, turbocharged, light-commercial vehicle and the baseline trip is the worldwide harmonized light-duty vehicles WLTC cycle. A detailed engine-mapping approach is used as the basis for the results, complemented with experimentally derived correction coefficients to account for engine transients. The properties of interest are (engine-out) NO and soot emissions, as well as fuel and energy consumption and CO2 emissions. Results from this study show that there is an aggregate increase in all properties, vis-à-vis the reference case (i.e., zero wind), if ambient wind is to be accounted for in road load calculation. Mean wind speeds for the different sites examined range from 14.6 km/h to 24.2 km/h. The average increase in the properties studied, across all sites, ranges from 0.22% up to 2.52% depending on the trip and the property (CO2, soot, NO, energy consumption) examined. Based on individual trip assessment, it was found that especially at high vehicle speeds where wind drag becomes the major road load force, CO2 emissions may increase by 28%, NO emissions by 22%, and soot emissions by 13% in the presence of strong headwinds. Moreover, it is demonstrated that the adverse effect of headwinds far exceeds the positive effect of tailwinds, thus explaining the overall increase in fuel/energy consumption as well as emissions, while also highlighting the shortcomings of the current certification procedure, which neglects ambient wind effects.

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