Bayesian Based Model Validation Method for Uncertain Multivariate Dynamic Systems under Virtual Prototype Environment

2012 ◽  
Vol 48 (05) ◽  
pp. 138 ◽  
Author(s):  
Zhenfei ZHAN
Keyword(s):  
Model Validation ◽  
Dynamic Systems ◽  
Virtual Prototype ◽  
Validation Method

An Enhanced Bayesian Based Model Validation Method for Dynamic Systems

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Zhenfei Zhan ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Yinghong Peng
Keyword(s):  
Dynamic System ◽  
Hypothesis Testing ◽  
Model Validation ◽  
Dynamic Systems ◽  
Computational Models ◽  
Principal Component ◽  
Functional Responses ◽  
New Approach ◽  
Validation Method ◽  
Critical Issues

Validation of computational models with multiple correlated functional responses requires the consideration of multivariate data correlation, uncertainty quantification and propagation, and objective robust metrics. This paper presents an enhanced Bayesian based model validation method together with probabilistic principal component analysis (PPCA) to address these critical issues. The PPCA is employed to handle multivariate correlation and to reduce the dimension of the multivariate functional responses. The Bayesian interval hypothesis testing is used to quantitatively assess the quality of a multivariate dynamic system. The differences between the test data and computer-aided engineering (CAE) results are extracted for dimension reduction through PPCA, and then Bayesian interval hypothesis testing is performed on the reduced difference data to assess the model validity. In addition, physics-based threshold is defined and transformed to the PPCA space for Bayesian interval hypothesis testing. This new approach resolves some critical drawbacks of the previous methods and adds some desirable properties of a model validation metric for dynamic systems, such as symmetry. Several sets of analytical examples and a dynamic system with multiple functional responses are used to demonstrate this new approach.

Development of a Comprehensive Validation Method for Dynamic Systems and Its Application on Vehicle Design

2015 ◽  
Vol 8 (3) ◽  
pp. 646-652 ◽  
Author(s):  
Junqi Yang ◽  
Zhenfei Zhan ◽  
Chong Chen ◽  
Yajing Shu ◽  
Ling Zheng ◽  
...  
Keyword(s):  
Dynamic Systems ◽  
Vehicle Design ◽  
Validation Method

Model Validation of Dynamic Engineering Models Under Uncertainty

2016 ◽  
Author(s):  
Zequn Wang ◽  
Yan Fu ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Wei Chen
Keyword(s):  
Dynamic Model ◽  
Model Validation ◽  
Dynamic Systems ◽  
Time Series Data ◽  
Integral Transform ◽  
Random Coefficients ◽  
Series Data ◽  
Input Space ◽  
Eigen Analysis ◽  
Engineering Models

Validating dynamic engineering models is critically important in practical applications by assessing the agreement between simulation results and experimental observations. Though significant progresses have been made, the existing metrics lack the capability of managing uncertainty in both simulations and experiments, which may stem from computer model instability, imperfection in material fabrication and manufacturing process, and variations in experimental conditions. In addition, it is challenging to validate a dynamic model aggregately over both the time domain and a model input space with data at multiple validation sites. To overcome these difficulties, this paper presents an area-based metric to systemically handle uncertainty and validate computational models for dynamic systems over an input space by simultaneously integrating the information from multiple validation sites. To manage the complexity associated with a high-dimensional data space, Eigen analysis is performed for the time series data from simulations at each validation site to extract the important features. A truncated Karhunen-Loève (KL) expansion is then constructed to represent the responses of dynamic systems, resulting in a set of uncorrelated random coefficients with unit variance. With the development of a hierarchical data fusion strategy, probability integral transform is then employed to pool all the resulting random coefficients from multiple validation sites across the input space into a single aggregated metric. The dynamic model is thus validated by calculating the cumulative area difference of the cumulative density functions. The proposed model validation metric for dynamic systems is illustrated with a mathematical example, a supported beam problem with stochastic loads, and real data from the vehicle occupant restraint system.

Bayesian Probabilistic PCA Approach for Model Validation of Dynamic Systems

2009 ◽  
Vol 2 (1) ◽  
pp. 555-563 ◽  
Author(s):  
Xiaomo Jiang ◽  
Ren-Jye Yang ◽  
Saeed Barbat ◽  
Para Weerappuli
Keyword(s):  
Model Validation ◽  
Dynamic Systems ◽  
Probabilistic Pca

Identification of a Model Helicopter’s Yaw Dynamics

2005 ◽  
Vol 127 (1) ◽  
pp. 140-145 ◽  
Author(s):  
Chen Haosheng ◽  
Chen Darong
Keyword(s):  
System Identification ◽  
Model Validation ◽  
Dynamic Systems ◽  
Model Parameters ◽  
Conventional System ◽  
Experiment Data ◽  
System Identification Method ◽  
Identification Method ◽  
Criterion A

To identify the micro helicopter’s yaw dynamics, the system identification method is used and is proved to be suitable according to the validation results. In order to strengthen the information of the dynamics and reduce the effect of the noise when processing the experiment data, the conventional system identification method is modified and a weighted criterion is investigated to estimate the model parameters. In calculating the factors of the weighted criterion, a perceptron is trained to allocate the factors automatically. The model validation result shows that the model derived by this kind of method can fit the measured outputs well. The modified system identification method would be useful in identifying dynamic systems which use the multiexperiment data.

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