Dynamic Cylinder Deactivation of ICE - Simulation Methodology

2021 ◽  
Author(s):  
Ondrej Bolehovsky ◽  
Oldrich Vitek
1991 ◽  
Author(s):  
Peter W. Glynn ◽  
Donald L. Iglehart

2012 ◽  
Vol 5 (2) ◽  
pp. 207-215 ◽  
Author(s):  
Rudolf Flierl ◽  
Frederic Lauer ◽  
Michael Breuer ◽  
Wilhelm Hannibal

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 661
Author(s):  
Alexandros T. Zachiotis ◽  
Evangelos G. Giakoumis

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.


2019 ◽  
Vol 11 (24) ◽  
pp. 7229
Author(s):  
Guofeng Ma ◽  
Jianyao Jia ◽  
Tiancheng Zhu ◽  
Shan Jiang

In order to overcome the difficulty in quantifying rework by traditional project schedule management tools, this study proposes an innovative method, namely improved Critical Chain Design Structure Matrix (ICCDSM). From the perspective of information flow, the authors firstly make assumptions on activity parameters and interactions between activities. After that, a genetic algorithm is employed to reorder the activity sequence, and a banding algorithm with consideration of resource constraints is used to identify concurrent activities. Then potential criticality is proposed to measure the importance of each activity, and the rework impact area is implicated to indicate potential rework windows. Next, two methods for calculating project buffer are employed. A simulation methodology is used to verify the proposed method. The simulation results illustrate that the ICCDSM method is capable of quantifying and visualizing rework and its impact, decreases iterations, and improves the completion probability. In this vein, this study provides a novel framework for rework management, which offers some insights for researchers and managers.


Sign in / Sign up

Export Citation Format

Share Document