Chapter 4. Conditional inversion and types of parametric change

2017 ◽  
pp. 57-77 ◽  
Author(s):  
Theresa Biberauer ◽  
Ian Roberts
Keyword(s):  
Parametric Change

A Finite Element Method for the Dynamic Analysis of Automatic Transmission Gear Shifting with a Four-Degree-of-Freedom Planetary Gearset Element

2003 ◽  
Vol 217 (6) ◽  
pp. 461-473 ◽  
Author(s):  
N Zhang ◽  
A Crowther ◽  
D K Liu ◽  
J Jeyakumaran
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Automatic Transmission ◽  
Free Vibration Analysis ◽  
Planetary Gear ◽  
Rigid Body Dynamics ◽  
Degree Of Freedom ◽  
Parametric Change ◽  
Stiffness And Damping ◽  
Element Method

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.

Mathematical Modeling of the Dynamics of a Two-Mass Gear System with Consideration of Shaft Compliances

2015 ◽  
Vol 770 ◽  
pp. 343-348 ◽  
Author(s):  
Mikhail Kurushin ◽  
Valeriy Balyakin
Keyword(s):  
Mathematical Modeling ◽  
Analytical Solution ◽  
Elastic System ◽  
Gear Wheel ◽  
Optimal Level ◽  
Gear System ◽  
Dynamic Excitation ◽  
Pinion Gear ◽  
Parametric Change ◽  
Meshing Process

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.

Efficient Adaptive Meshing of Parametric Models

2001 ◽  
Vol 1 (4) ◽  
pp. 366-375 ◽  
Author(s):  
Alla Sheffer ◽  
Alper U¨ngo¨r
Keyword(s):  
Finite Element ◽  
Design Process ◽  
Parametric Model ◽  
Parametric Modeling ◽  
Boundary Representation ◽  
Finite Element Mesh ◽  
Parametric Models ◽  
Adaptive Meshing ◽  
Element Mesh ◽  
Parametric Change

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.

scholarly journals Non-Parametric Change-Point Method for Differential Gene Expression Detection

PLoS ONE ◽  
2011 ◽  
Vol 6 (5) ◽  
pp. e20060 ◽  
Author(s):  
Yao Wang ◽  
Chunguo Wu ◽  
Zhaohua Ji ◽  
Binghong Wang ◽  
Yanchun Liang
Keyword(s):  
Gene Expression ◽  
Differential Gene Expression ◽  
Change Point ◽  
Point Method ◽  
Parametric Change ◽  
Differential Gene ◽  
Non Parametric

scholarly journals Robustness of Neural Network Emulations of Radiative Transfer Parameterizations in a State-of-the-Art General Circulation Model

2021 ◽  
Author(s):  
Alexei Belochitski ◽  
Vladimir Krasnopolsky
Keyword(s):  
Neural Network ◽  
Radiative Transfer ◽  
Network Architecture ◽  
General Circulation ◽  
State Of The Art ◽  
Circulation Model ◽  
Set Design ◽  
Neural Network Architecture ◽  
Parametric Change ◽  
Model Components

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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