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190921s2014 gw o 000 0 eng d |
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|a 9783832591632
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|a 383259163X
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|a (OCoLC)1112422358
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|a QA402.3
|b .S744 2014
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|a HCDD
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|a Steffes-lai, Daniela.
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|a Approximation Methods for High Dimensional Simulation Results :
|b Parameter Sensitivity Analysis and Propagation of Variations for Process Chains.
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|a Berlin :
|b Logos Verlag Berlin,
|c 2014.
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|a 1 online resource (232 pages)
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|a text
|b txt
|2 rdacontent
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|a computer
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|2 rdamedia
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|a online resource
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|a Print version record.
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|a Intro; 1 Introduction; 1.1 Context; 1.2 Main Focus and Structure; 2 Notation and Fundamentals; 2.1 Terminology; 2.2 Fundamentals and General Approaches; 2.2.1 Stochastics; 2.2.2 Interpolation; 2.2.3 Mapping; 2.3 Sheet Metal Forming Processes with Example; 3 Mathematical Concepts; 3.1 Design of Experiments; 3.2 Sensitivity Analysis; 3.2.1 Local Methods; 3.2.2 Global Methods; 3.3 Dimension Reduction Methods; 3.3.1 Clustering; 3.3.2 Principal Component Analysis Using Singular Value Decomposition; 3.3.3 Nonlinear Dimension Reduction Methods; 3.4 Metamodels; 3.5 Stochastic Finite Element Methods
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|a 3.5.1 Stochastic Galerkin Approach3.5.2 Stochastic Collocation Method; 4 Parameter Classification Using Sensitivity Analysis; 4.1 Importance and Nonlinearity Classes; 4.1.1 Linear Importance Classes; 4.1.2 Nonlinearity Classes; 4.1.3 Total Importance Classes; 4.1.4 Application of the Parameter Classification; 4.2 Clustering Using the Nonlinearity Measure; 4.3 Efficiency; 4.4 Conclusions; 5 Processing of the Database; 5.1 Parameter Space Dimension Reduction; 5.2 Iterative Extension of the Database; 5.3 Ensemble Compression of the Database; 6 Forecast Model and Propagation of Variations
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|a 6.1 Approximation of New Designs6.1.1 Radial Basis Function Metamodel Accelerated by a Singular Value Decomposition; 6.1.2 Dealing with Deleted Mesh Elements; 6.2 Computation of Statistics; 6.3 Propagation of All Relevant Scatter Information to the Next Processing Step; 6.4 Quality Control; 6.5 Efficiency; 6.6 Conclusions; 7 Benchmarks and Industrial Applications; 7.1 Numerical Comparison Between the New Methodology and a Stochastic Collocation Method; 7.1.1 Overview of the Model Problem; 7.1.2 Results of the Parameter Classification
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|a 7.1.3 Comparison Between the Results of a Collocation Method and the Accelerated Metamodel Approach7.2 Forming of a Pan with Secondary Design Elements; 7.2.1 Forecast Models; 7.2.2 Computation of Statistics; 7.2.3 Conclusions; 7.3 Process Chain Forming-to-Crash; 7.3.1 ZStE340 Metal Blank of a B-Pillar; 7.3.2 Parameter Classification of the Forming Step; 7.3.3 Forecast Models; 7.3.4 Parameter Classification of the Crash Processing Step; 7.3.5 Forecast Model Taking the Forming History Into Account; 7.3.6 Conclusions; 8 Conclusions and Future Directions; Bibliography; List of Figures
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|a List of TablesAcronyms; List of Symbols
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|a Annotation
|b This work addresses the analysis of a sequential chain of processing steps, which is particularly important for the manufacture of robust product components. In each processing step, the material properties may have changed and distributions of related characteristics, for example, strains, may become inhomogeneous. For this reason, the history of the process including design-parameter uncertainties becomes relevant for subsequent processing steps. Therefore, we have developed a methodology, called PRO-CHAIN, which enables an efficient analysis, quantification, and propagation of uncertainties for complex process chains locally on the entire mesh. This innovative methodology has the objective to improve the overall forecast quality, specifically, in local regions of interest, while minimizing the computational effort of subsequent analysis steps. We have demonstrated the benefits and efficiency of the methodology proposed by means of real applications from the automotive industry.
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|a Sensitivity theory (Mathematics)
|x Simulation methods.
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|i Print version:
|a Steffes-lai, Daniela.
|t Approximation Methods for High Dimensional Simulation Results : Parameter Sensitivity Analysis and Propagation of Variations for Process Chains.
|d Berlin : Logos Verlag Berlin, ©2014
|z 9783832536961
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| 856 |
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|u https://ebookcentral.proquest.com/lib/holycrosscollege-ebooks/detail.action?docID=5850403
|y Click for online access
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|a EBC-AC
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|b HCD
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