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Preface |
5 |
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Contents |
7 |
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Part I Methods |
9 |
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Calculation of Chemical Equilibria in Multi-Phase: Multicomponent Systems |
10 |
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1 Introduction |
10 |
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2 Problem Formulation |
12 |
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2.1 Non-Ideal Gibbs Function |
12 |
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2.2 Stoichiometric Constraints |
13 |
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2.3 The Optimization Problem |
15 |
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3 Methodology for the Calculation of Chemical Equilibria |
16 |
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3.1 Reformulation of the Minimization Problem |
17 |
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3.2 Discretization of the H-Problem |
18 |
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3.3 Corrector Step |
19 |
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4 Automated Detection of Miscibility Gaps |
20 |
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5 Results |
23 |
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5.1 Gibbs Free Energy Minimization Using BePhaSys |
23 |
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5.2 Calculation of Two-Dimensional Phase Diagrams: Interpolation and Parallelization |
23 |
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Appendix: The Gibbs Free Energy Function |
29 |
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References |
30 |
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LC-GAP: Localized Coulomb Descriptors for the Gaussian Approximation Potential |
32 |
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1 Introduction |
32 |
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2 Potential Energy Prediction Through Machine Learning |
33 |
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2.1 The GAP Framework and Gaussian Process Regression |
34 |
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2.2 Localized Coulomb Matrix Descriptors |
35 |
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3 Results |
37 |
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3.1 Comparison of Descriptor Functions on QM7 |
39 |
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3.2 Larger Datasets and Prediction of Multiple Properties |
41 |
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3.3 Distribution of Individual Atomic Contributions |
44 |
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4 Conclusions and Future Work |
47 |
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References |
48 |
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River Bed Morphodynamics: Metamodeling, Reliability Analysis, and Visualization in a Virtual Environment |
50 |
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1 Introduction |
50 |
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2 RBF Metamodel |
53 |
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3 Quantile Estimation |
55 |
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3.1 Sensitivity-Based Approach |
55 |
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3.1.1 First-Order Approximation |
55 |
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3.1.2 Second-Order Approximation |
55 |
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3.2 Monte Carlo |
56 |
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3.3 Weighted Monte Carlo |
57 |
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3.4 Quasi-Monte Carlo (QMC) |
57 |
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3.5 Quasi-Random Splines (QRS) |
58 |
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4 Numerical Tests |
59 |
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5 Visualization in Virtual Environment |
62 |
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6 Conclusion |
65 |
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References |
65 |
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Cooling Circuit Simulation I: Modeling |
67 |
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1 Introduction |
67 |
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2 Network |
68 |
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3 Water Pipes |
69 |
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3.1 Continuum Mechanics |
69 |
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3.2 Simplifying Assumptions |
70 |
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3.3 Discretization and Regularization |
72 |
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4 Further Devices |
75 |
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4.1 Resistors and Valves |
75 |
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4.2 Pumps |
77 |
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4.3 Heat Exchangers |
79 |
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5 Element Control |
82 |
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6 Conclusion |
84 |
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References |
85 |
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Part II Products |
86 |
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Algebraic Multigrid: From Academia to Industry |
87 |
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1 Introduction |
87 |
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2 From Geometric to Algebraic Multigrid |
89 |
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3 The Early Phase of Algebraic Multigrid (1982–1987) |
91 |
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3.1 The First Documented AMG Application |
92 |
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3.2 The Basics of `Classical' AMG |
93 |
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4 The Renaissance of AMG (1995–2000) |
95 |
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4.1 Resumption of Major Research on AMG |
95 |
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4.2 Towards Industry |
96 |
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4.2.1 Computational Fluid Dynamics |
97 |
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4.2.2 Streamline Approach in Oil Reservoir Simulation |
99 |
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5 The Main AMG Development Phase (2000–Today) |
101 |
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5.1 The General Trend |
101 |
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5.2 Bridging the Gap |
102 |
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5.3 SAMG for Coupled PDE Systems |
105 |
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5.3.1 Unknown-Based Approach |
105 |
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5.3.2 Point-Based Approach |
106 |
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5.3.3 Status of the Solver Framework SAMG |
106 |
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6 Industry-Driven Applications |
107 |
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6.1 Semiconductor Applications |
107 |
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6.2 Multi-Ion Transport and Reaction |
110 |
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6.3 Oil Reservoir Simulation |
111 |
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6.3.1 The Reservoir Simulation Models |
113 |
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6.3.2 Fully Implicit Methods |
114 |
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7 Summary, Conclusions and Lessons Learned |
119 |
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References |
121 |
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Parallel Algebraic Multigrid |
124 |
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1 Introduction |
124 |
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2 Challenges Imposed by Parallel Computer Architectures |
126 |
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2.1 Single Core Performance: CPU Clock Speed and Memory Frequency |
126 |
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2.2 Multi-Core CPUs and Shared Memory Parallelism |
127 |
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2.2.1 Multi-Core and Memory Access |
128 |
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2.2.2 Intrinsically Serial Components |
129 |
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2.2.3 Race Conditions |
129 |
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2.2.4 Multiple Sockets |
130 |
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2.3 Distributed Memory Parallelism |
130 |
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3 How SAMG Counters the HPC Challenges |
131 |
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3.1 Tuning and Parallelization of Smoothing |
132 |
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3.2 Ruge-Stüben Coarsening |
134 |
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3.3 Coarse Grid Solution |
135 |
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4 SCAI's Parallel SAMG Solver Library |
136 |
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References |
137 |
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MpCCI: Neutral Interfaces for Multiphysics Simulations |
138 |
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1 Introduction |
138 |
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2 MpCCI CouplingEnvironment |
139 |
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2.1 Aero-Elasticity and Fluid-Structure-Interaction |
141 |
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2.1.1 Wing and Spoiler Design |
141 |
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2.1.2 Hydraulic Pump Layout |
142 |
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2.2 Thermal and Vibration Loads in Turbomachinery |
143 |
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2.2.1 Thermal Loads on Ceramic Impeller |
143 |
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2.2.2 Life-Time Estimation of Turbine Blades |
144 |
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2.3 Vehicle Dynamics and Nonlinear Component Behavior |
144 |
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2.3.1 Driving Over Obstacles |
144 |
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2.3.2 Wading Simulation for Off-Road Vehicles |
144 |
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2.4 Automotive Thermal Management |
146 |
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2.4.1 Automotive Thermal Management for Full Vehicles |
146 |
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2.4.2 Automotive Thermal Management for Vehicle Manifolds |
146 |
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2.5 Component Design in Electrical Engineering |
147 |
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2.5.1 Cooling of a 3-Phase Transformer |
147 |
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2.5.2 Electric Arc in Switching Devices |
147 |
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3 MpCCI FSIMapper |
148 |
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4 MpCCI Mapper Solution for Integrated Simulation Workflows |
149 |
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4.1 Passive Safety |
150 |
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4.2 Forming Tools and Material Properties |
151 |
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4.2.1 Lightweight Stamping Tools: Use Forming Loads in Structural Optimization |
151 |
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4.2.2 Validation of Material Model Parameters: Compare Forming Results and Experimental Data |
151 |
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4.3 Composite Structures and Plastic Components |
152 |
|
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4.3.1 CFRP Workflows: From Draping via Mulling and Curing to Structural Analysis |
152 |
|
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4.3.2 Structural Integrity of Blow Moulded Plastic Components |
153 |
|
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5 Conclusion |
153 |
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References |
153 |
|
|
Cooling Circuit Simulation II: A Numerical Example |
155 |
|
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1 Introduction |
155 |
|
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2 Application |
156 |
|
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2.1 Cooling System |
156 |
|
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2.2 Circuit Basics and Example |
156 |
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3 Concept and Software |
163 |
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3.1 Framework and Components |
163 |
|
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3.2 Semi-Automatic Model Creation with Schemparser |
165 |
|
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3.3 Device Modeling and Sensor Mapping |
166 |
|
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3.4 Collection of Measurement Data with PowerDAM |
167 |
|
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3.5 Nonlinear Problem Setup and Solution with MYNTS |
168 |
|
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4 Numerical Tests |
170 |
|
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4.1 Simplified Heat Exchanger |
170 |
|
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4.2 Logarithmic Mean Temperature Difference |
177 |
|
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5 Conclusion |
179 |
|
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References |
181 |
|
|
The LAMA Approach for Writing Portable Applications on Heterogenous Architectures |
183 |
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1 Introduction |
183 |
|
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2 LAMA |
184 |
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2.1 Heterogeneous Memory |
186 |
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2.2 Heterogeneous Kernel |
188 |
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2.3 Task Parallelism |
188 |
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2.4 Distributed Memory Support |
190 |
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2.5 Matrices and Vectors |
191 |
|
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2.6 Solver Framework |
191 |
|
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2.7 Extensibility and Maintainability |
193 |
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3 Performance Comparison |
194 |
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4 Summary |
197 |
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Appendix |
198 |
|
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Test Environment |
198 |
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Test Matrices |
199 |
|
|
References |
200 |
|
|
ModelCompare |
201 |
|
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1 Introduction |
201 |
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2 Development History |
202 |
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3 Capabilities |
202 |
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3.1 Detection of Geometry Changes |
203 |
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3.2 Detection of MultiParts |
204 |
|
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3.3 Spotwelds and Rigid Body Elements |
205 |
|
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3.4 Detection of Material-ID and Thickness Changes |
206 |
|
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4 Outlook |
207 |
|
|
References |
207 |
|
|
Rapid Enriched Simulation Application Development with PUMA |
208 |
|
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1 Introduction |
208 |
|
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2 Partition of Unity Methods |
209 |
|
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3 PUMA Framework Design |
210 |
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4 Application Examples |
212 |
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5 Concluding Remarks |
225 |
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References |
226 |
|
|
Part III Applications and Show Cases |
228 |
|
|
Applying CFD for the Design of an Air-Liquid Interface In-Vitro Testing Method for Inhalable Compounds |
229 |
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1 Introduction |
229 |
|
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2 In-Vitro Air-Liquid Interface |
230 |
|
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3 Simulating the Aerosol Conduction System |
231 |
|
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4 Simulating the Liquid Supply System |
237 |
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4.1 Clogging in Liquid Channels |
239 |
|
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5 Simulating an Aerosol Sampling Box |
240 |
|
|
6 Conclusions |
243 |
|
|
References |
243 |
|
|
A Mapping Procedure for the Computation of Flow-Induced Vibrations in Turbomachinery |
244 |
|
|
1 Introduction |
244 |
|
|
2 Nonlinear Harmonic Method |
245 |
|
|
3 Mapping of Pressure Excitations |
246 |
|
|
3.1 Periodic Models and Nodal Diameters |
248 |
|
|
3.2 Deriving Excitation and Responding Shape |
251 |
|
|
3.3 Summary |
252 |
|
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4 Application Example |
252 |
|
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4.1 Harmonic CFD Simulation |
254 |
|
|
4.2 Mapping |
254 |
|
|
4.3 Harmonic Structural Analysis |
257 |
|
|
5 Conclusion |
259 |
|
|
References |
261 |
|
|
Molecular Dynamics Simulation of Membrane Free Energy Profiles Using Accurate Force Field for Ionic Liquids |
263 |
|
|
1 Introduction |
263 |
|
|
2 Computational Methods |
264 |
|
|
2.1 Simulation Details |
264 |
|
|
2.1.1 Technical Details |
264 |
|
|
2.1.2 Force Field Development for [C2MIM][EtSO4] |
265 |
|
|
2.2 Umbrella Sampling |
267 |
|
|
3 Results and Discussion |
269 |
|
|
3.1 Force Field Development for [C2MIM][EtSO4] |
269 |
|
|
3.1.1 Density |
269 |
|
|
3.1.2 Self-Diffusion Coefficients |
270 |
|
|
3.1.3 Heat of Vaporization |
270 |
|
|
3.1.4 Shear Viscosity |
271 |
|
|
3.2 Free Energy Profiles |
271 |
|
|
4 Outlook and Conclusion |
274 |
|
|
4.1 Outlook: Towards Fully Automated Force Field Development |
274 |
|
|
4.1.1 Case Study: Automated Parameterization of Ethylene Oxide |
277 |
|
|
4.2 Conclusion |
279 |
|
|
References |
279 |
|
|
The cloud4health Project: Secondary Use of Clinical Data with Secure Cloud-Based Text Mining Services |
283 |
|
|
1 Introduction |
283 |
|
|
2 Developing a Secure Cloud-Solution for Medicine |
285 |
|
|
2.1 Existing Cloud Solutions for Medicine |
285 |
|
|
2.2 Requirements for Cloud Infrastructures Arising from Patient Data Processing |
287 |
|
|
2.3 Security Mechanisms |
288 |
|
|
2.4 Secure Cloud Infrastructure |
290 |
|
|
2.4.1 Secure Clinical Gateway to the Cloud |
291 |
|
|
2.4.2 Data Processing Flow |
292 |
|
|
2.4.3 End-to-End Encryption |
293 |
|
|
2.4.4 Multi-Tenancy and No Data Persistence |
295 |
|
|
3 Clinical Text Mining Solutions |
297 |
|
|
3.1 Short Literature Overview |
297 |
|
|
3.2 General Architecture of the Text Mining Services |
299 |
|
|
3.3 Overview Use Cases |
301 |
|
|
3.3.1 General Use Case Process Model |
302 |
|
|
3.4 Mining Endoprosthetic Surgery Reports |
303 |
|
|
3.5 Mining Pathology Reports |
306 |
|
|
4 Discussion |
309 |
|
|
References |
311 |
|
|
Dimensionality Reduction for the Analysis of Time Series Data from Wind Turbines |
314 |
|
|
1 Introduction |
314 |
|
|
2 Time Series Characteristics in Wind Energy |
316 |
|
|
2.1 Numerical Simulations of Wind Turbines |
316 |
|
|
2.2 Condition Monitoring of Wind Turbines |
318 |
|
|
3 Exploration of Time Series Data from Numerical Simulations |
319 |
|
|
3.1 Virtual Sensor Data from Wind Turbine Simulations |
319 |
|
|
3.2 Nonlinear Dimensionality Reduction for Time Series Analysis |
321 |
|
|
3.3 Diffusion Maps |
322 |
|
|
3.4 Numerical Results |
323 |
|
|
4 Anomaly Detection Based on Linear Dimensionality Reduction for Condition Monitoring Sensor Data from Wind Turbines |
326 |
|
|
4.1 Sensor Data from Rotor Blades |
326 |
|
|
4.1.1 Pre-processing |
327 |
|
|
4.2 Anomaly Detection in Sensor Data |
329 |
|
|
4.2.1 Model of Undamaged State |
329 |
|
|
4.2.2 Deviation from the Undamaged State |
330 |
|
|
4.3 Methodology for Damage Detection |
331 |
|
|
4.4 Numerical Results |
332 |
|
|
5 Conclusions |
335 |
|
|
References |
335 |
|
|
Energy-Efficiency and Performance Comparison of Aerosol Optical Depth Retrieval on Distributed Embedded SoC Architectures |
337 |
|
|
1 Introduction |
337 |
|
|
2 Method |
338 |
|
|
3 Implementation |
340 |
|
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4 Embedded Low-Energy System |
342 |
|
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5 Benchmarks |
344 |
|
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5.1 Benchmark Environment |
345 |
|
|
5.2 Performance Benchmarks |
345 |
|
|
5.3 Energy Benchmarks |
347 |
|
|
6 Discussion |
353 |
|
|
7 Outlook |
353 |
|
|
References |
354 |
|
|
Part IV A Short History |
355 |
|
|
The Fraunhofer Institute for Algorithms and ScientificComputing SCAI |
356 |
|
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1 Foundation of the GMD, the First Decade (1968–1977) |
358 |
|
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2 Numerical Simulation, Multigrid and Parallel Computing (1978–1991) |
359 |
|
|
3 SCAI: Algorithms and Scientific Computing (1992–2001) |
362 |
|
|
4 SCAI as a Fraunhofer Institute, the First Years (2001–2009) |
366 |
|
|
5 New Fields of Research and New Business Areas (2010–2016) |
368 |
|
|
References |
369 |
|