Optimal Design of Experiments for Estimating Parameters of a Vehicle Dynamics Simulation Model

The calibration of complex simulation models for vehicle component and controller development usually relies on numerical methods. In this contribution, a two-level optimization scheme for estimating unknown model parameters in a commercial real-time capable vehicle dynamics program is proposed. In order to increase the reliability of the model coefficients estimated from reference data, the measuring test is improved by methods for the optimal design of experiments. Specifically, the control variables of the experimental setup are adjusted in such a way as to maximize the sensitivity of the parameters in demand with respect to the objective function. The numerical results show that this two-level optimization scheme is capable of estimating the parameters of a multibody suspension model.

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Optimal design of experiments with simulation models of nearly saturated queues

Journal of Statistical Planning and Inference ◽

10.1016/s0378-3758(99)00061-0 ◽

2000 ◽

Vol 85 (1-2) ◽

pp. 19-26 ◽

Cited By ~ 11

Author(s):

R.C.H. Cheng ◽

J.P.C. Kleijnen ◽

V.B. Melas

Keyword(s):

Optimal Design ◽

Design Of Experiments ◽

Simulation Models ◽

Optimal Design Of Experiments

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Identification of model uncertainty via optimal design of experiments applied to a mechanical press

Optimization and Engineering ◽

10.1007/s11081-021-09600-8 ◽

2021 ◽

Author(s):

Tristan Gally ◽

Peter Groche ◽

Florian Hoppe ◽

Anja Kuttich ◽

Alexander Matei ◽

...

Keyword(s):

Optimal Design ◽

Design Of Experiments ◽

Model Uncertainty ◽

Confidence Regions ◽

Specific Model ◽

Data Uncertainty ◽

Parameter Estimates ◽

Model Parameters ◽

Optimal Design Of Experiments ◽

Almost All

AbstractIn engineering applications almost all processes are described with the help of models. Especially forming machines heavily rely on mathematical models for control and condition monitoring. Inaccuracies during the modeling, manufacturing and assembly of these machines induce model uncertainty which impairs the controller’s performance. In this paper we propose an approach to identify model uncertainty using parameter identification, optimal design of experiments and hypothesis testing. The experimental setup is characterized by optimal sensor positions such that specific model parameters can be determined with minimal variance. This allows for the computation of confidence regions in which the real parameters or the parameter estimates from different test sets have to lie. We claim that inconsistencies in the estimated parameter values, considering their approximated confidence ellipsoids as well, cannot be explained by data uncertainty but are indicators of model uncertainty. The proposed method is demonstrated using a component of the 3D Servo Press, a multi-technology forming machine that combines spindles with eccentric servo drives.

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Optimal Design of Experiments for Identifying the Model Parameters of Gyroscope on a Centrifuge and Vibration Table Compound Test Syste

First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06) ◽

10.1109/icicic.2006.490 ◽

2006 ◽

Author(s):

Wang Yuegang

Keyword(s):

Optimal Design ◽

Design Of Experiments ◽

Model Parameters ◽

Optimal Design Of Experiments ◽

Compound Test

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Maximal Power Tests for Detecting Defects in Meiotic Recombination

Genetics ◽

10.1093/genetics/161.3.1333 ◽

2002 ◽

Vol 161 (3) ◽

pp. 1333-1337

Author(s):

Thomas I Milac ◽

Frederick R Adler ◽

Gerald R Smith

Keyword(s):

Optimal Design ◽

Design Of Experiments ◽

Meiotic Recombination ◽

Region Of Interest ◽

Genetic Distances ◽

Genomic Region ◽

Maximal Power ◽

Optimal Design Of Experiments ◽

Crossover Interference ◽

Single Interval

Abstract We have determined the marker separations (genetic distances) that maximize the probability, or power, of detecting meiotic recombination deficiency when only a limited number of meiotic progeny can be assayed. We find that the optimal marker separation is as large as 30–100 cM in many cases. Provided the appropriate marker separation is used, small reductions in recombination potential (as little as 50%) can be detected by assaying a single interval in as few as 100 progeny. If recombination is uniformly altered across the genomic region of interest, the same sensitivity can be obtained by assaying multiple independent intervals in correspondingly fewer progeny. A reduction or abolition of crossover interference, with or without a reduction of recombination proficiency, can be detected with similar sensitivity. We present a set of graphs that display the optimal marker separation and the number of meiotic progeny that must be assayed to detect a given recombination deficiency in the presence of various levels of crossover interference. These results will aid the optimal design of experiments to detect meiotic recombination deficiency in any organism.

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