Lateral Dynamics Optimization of a Conventional Railcar

1975 ◽  
Vol 97 (3) ◽  
pp. 293-299 ◽  
Author(s):  
N. K. Cooperrider ◽  
J. J. Cox ◽  
J. K. Hedrick

The attempt to develop a railway vehicle that can operate in the 150 to 300-mph(240 to 480-km/h) speed regime is seriously hampered by the problems of ride comfort, curve negotiation, and “hunting.” This latter phenomena involves sustained lateral oscillations that occur above certain critical forward velocities and cause large dynamic loads between the wheels and track as well as contributing to passenger discomfort. This paper presents results of an initial effort to solve these problems by utilizing optimization procedures to design a high speed railway vehicle. This study indicates that the problem is more easily treated as a constrained optimization problem than as an unconstrained problem with several terms in the objective function. In the constrained optimization problem, the critical “hunting” speed was maximized subject to constraints on 1) the acceleration of the car body, 2) the suspension stroke length, and 3) the maximum suspension stroke while negotiating a curve. A simple, three degree-of-freedom model of the rail vehicle was used for this study. Solutions of this constrained problem show that beyond a minimum yaw stiffness between truck and car body the operating speed remains nearly constant. Thus, above this value, the designer may trade off yaw stiffness, wheel tread conicity and stability margin.

2020 ◽  
Vol 12 (5) ◽  
pp. 27
Author(s):  
Bouchta x RHANIZAR

We consider the constrained optimization problem  defined by: $$f(x^*) = \min_{x \in  X} f(x) \eqno (1)$$ where the function  $f$ : $ \pmb{\mathbb{R}}^{n} \longrightarrow \pmb{\mathbb{R}}$ is convex  on a closed convex set X. In this work, we will give a new method to solve problem (1) without bringing it back to an unconstrained problem. We study the convergence of this new method and give numerical examples.


Author(s):  
Chi Liu ◽  
David Thompson ◽  
Michael J Griffin ◽  
Mani Entezami

The operational speeds of passenger trains have been increasing and now often exceed 300 km/h. Higher speeds can lead to increased vibration and reduced ride comfort for railway passengers. This study investigates the combined effect of speed and track geometry on vibration discomfort in high-speed trains. Railway vehicle dynamic models with various levels of complexity are used, with the measured geometry of a section of a high-speed track as an input. The models have been calibrated with vibration measurements carried out in a train running over this section of the track and then applied to predict the vibration discomfort at increased speeds. To evaluate the vibration discomfort at speeds up to 400 km/h, information on track geometry should include wavelengths up to at least 150 m. Vertical irregularities have the greatest effect at all speeds but lateral irregularities are also important. Both the vertical and lateral irregularities of a high-speed track should be controlled at wavelengths of 50–100 m that excite rigid modes of the car body, corresponding to frequencies of typically 1–2 Hz. Additionally, vertical irregularities with wavelengths of 5–12 m that excite the fundamental flexible mode of the car body, typically around 10–15 Hz, should also be controlled. The effects of cant, the rates of change of cant, and the radius of vertical curves are also evaluated although they only have a small effect on vibration discomfort.


Author(s):  
Gabriele Eichfelder ◽  
Kathrin Klamroth ◽  
Julia Niebling

AbstractA major difficulty in optimization with nonconvex constraints is to find feasible solutions. As simple examples show, the $$\alpha $$ α BB-algorithm for single-objective optimization may fail to compute feasible solutions even though this algorithm is a popular method in global optimization. In this work, we introduce a filtering approach motivated by a multiobjective reformulation of the constrained optimization problem. Moreover, the multiobjective reformulation enables to identify the trade-off between constraint satisfaction and objective value which is also reflected in the quality guarantee. Numerical tests validate that we indeed can find feasible and often optimal solutions where the classical single-objective $$\alpha $$ α BB method fails, i.e., it terminates without ever finding a feasible solution.


Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2107 ◽  
Author(s):  
Min-Rong Chen ◽  
Huan Wang ◽  
Guo-Qiang Zeng ◽  
Yu-Xing Dai ◽  
Da-Qiang Bi

The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.


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