Chapter 4. Conditional inversion and types of parametric change

Author(s):  
Theresa Biberauer ◽  
Ian Roberts
Keyword(s):  
Author(s):  
N Zhang ◽  
A Crowther ◽  
D K Liu ◽  
J Jeyakumaran

A dynamic model of a passenger car automatic transmission and driveline is developed for simulating transient torsional vibration in gearshifts. A finite element method is proposed for presenting the transient dynamics of the parametric system, element matrices are defined and then global inertial, stiffness and damping matrices are formulated corresponding to the defined global coordinate vectors. A four-degree-of-freedom matrix element is developed that describes the rigid body dynamics of the planetary gear set and is then integrated with the driveline system; this element captures the parametric change while the transmission speed ratios vary over gearshifts. Free vibration analysis and a transient 2-3 upshift simulation are discussed and results presented.


2015 ◽  
Vol 770 ◽  
pp. 343-348 ◽  
Author(s):  
Mikhail Kurushin ◽  
Valeriy Balyakin

Outlined in this study is an analytical solution of the problem of dynamic excitation of two-mass elastic system by parametric change in the stiffness of teeth during the meshing process with consideration of the compliance of shafts. It is shown that the main source of excitation in a gear system is the central pinion gear. Introduction of an optimal level of multi-pitch in the gear wheel lowers vibrations to minimum.


2001 ◽  
Vol 1 (4) ◽  
pp. 366-375 ◽  
Author(s):  
Alla Sheffer ◽  
Alper U¨ngo¨r

Parametric modeling is becoming the representation of choice for most modern solid modelers. However, when generating the finite-element mesh of the model for simulation and analysis, most meshing tools ignore the parametric information and use only the boundary representation of the model for meshing. This results in re-meshing the model basically from scratch each time a parametric change is instantiated, which happens numerous times throughout the design process. In this paper we look at ways to use the parametric information during the meshing procedure to prevent unnecessary re-meshing. The paper examines existing meshing techniques developed for other purposes, which can be applied to this problem. It also suggests several new mesh modification techniques specifically designed for efficient mesh adjustment after parametric model changes.


PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e20060 ◽  
Author(s):  
Yao Wang ◽  
Chunguo Wu ◽  
Zhaohua Ji ◽  
Binghong Wang ◽  
Yanchun Liang

2021 ◽  
Author(s):  
Alexei Belochitski ◽  
Vladimir Krasnopolsky

Abstract. The ability of Machine-Learning (ML) based model components to generalize to the previously unseen inputs, and the resulting stability of the models that use these components, has been receiving a lot of recent attention, especially when it comes to ML-based parameterizations. At the same time, ML-based emulators of existing parameterizations can be stable, accurate, and fast when used in the model they were specifically designed for. In this work we show that shallow-neural-network-based emulators of radiative transfer parameterizations developed almost a decade ago for a state-of-the-art GCM are robust with respect to the substantial structural and parametric change in the host model: when used in two seven month-long experiments with the new model, they not only remain stable, but generate realistic output. Aspects of neural network architecture and training set design potentially contributing to stability of ML-based model components are discussed.


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