- •Table of Contents
- •Chapter 1: Probabilistic Design
- •1.1. Understanding Probabilistic Design
- •1.1.1. Traditional (Deterministic) vs. Probabilistic Design Analysis Methods
- •1.1.2. Reliability and Quality Issues
- •1.2. Probabilistic Design Terminology
- •1.3. Using Probabilistic Design
- •1.3.1. Create the Analysis File
- •1.3.1.1. Example Problem Description
- •1.3.1.2. Build the Model Parametrically
- •1.3.1.3. Obtain the Solution
- •1.3.1.4. Retrieve Results and Assign as Output Parameters
- •1.3.1.5. Prepare the Analysis File
- •1.3.2. Establish Parameters for Probabilistic Design Analysis
- •1.3.3. Enter the PDS and Specify the Analysis File
- •1.3.4. Declare Random Input Variables
- •1.3.5. Visualize Random Input Variables
- •1.3.6. Specify Correlations Between Random Variables
- •1.3.7. Specify Random Output Parameters
- •1.3.8. Select a Probabilistic Design Method
- •1.3.8.1. Probabilistic Method Determination Wizard
- •1.3.9. Execute Probabilistic Analysis Simulation Loops
- •1.3.9.1. Probabilistic Design Looping
- •1.3.9.2. Serial Analysis Runs
- •1.3.9.3. PDS Parallel Analysis Runs
- •1.3.9.3.1. Machine Configurations
- •1.3.9.3.1.1. Choosing Slave Machines
- •1.3.9.3.1.2. Using the Remote Shell Option
- •1.3.9.3.1.3. Using the Connection Port Option
- •1.3.9.3.1.4. Configuring the Master Machine
- •1.3.9.3.1.5. Host setup using port option
- •1.3.9.3.1.6. Host and Product selection for a particular analysis
- •1.3.9.3.2. Files Needed for Parallel Run
- •1.3.9.3.3. Controlling Server Processes
- •1.3.9.3.4. Initiate Parallel Run
- •1.3.10. Fit and Use Response Surfaces
- •1.3.10.1. About Response Surface Sets
- •1.3.10.2. Fitting a Response Surface
- •1.3.10.3. Plotting a Response Surface
- •1.3.10.4. Printing a Response Surface
- •1.3.10.5. Generating Monte Carlo Simulation Samples on the Response Surfaces
- •1.3.11. Review Results Data
- •1.3.11.1. Viewing Statistics
- •1.3.11.2. Viewing Trends
- •1.3.11.3. Creating Reports
- •1.4. Guidelines for Selecting Probabilistic Design Variables
- •1.4.1. Choosing and Defining Random Input Variables
- •1.4.1.1. Random Input Variables for Monte Carlo Simulations
- •1.4.1.2. Random Input Variables for Response Surface Analyses
- •1.4.1.3. Choosing a Distribution for a Random Variable
- •1.4.1.3.1. Measured Data
- •1.4.1.3.2. Mean Values, Standard Deviation, Exceedence Values
- •1.4.1.3.3. No Data
- •1.4.1.4. Distribution Functions
- •1.4.2. Choosing Random Output Parameters
- •1.5. Probabilistic Design Techniques
- •1.5.1. Monte Carlo Simulations
- •1.5.1.1. Direct Sampling
- •1.5.1.2. Latin Hypercube Sampling
- •1.5.1.3. User-Defined Sampling
- •1.5.2. Response Surface Analysis Methods
- •1.5.2.1. Central Composite Design Sampling
- •1.5.2.2. Box-Behnken Matrix Sampling
- •1.5.2.3. User-Defined Sampling
- •1.6. Postprocessing Probabilistic Analysis Results
- •1.6.1. Statistical Postprocessing
- •1.6.1.1. Sample History
- •1.6.1.2. Histogram
- •1.6.1.3. Cumulative Distribution Function
- •1.6.1.4. Print Probabilities
- •1.6.1.5. Print Inverse Probabilities
- •1.6.2. Trend Postprocessing
- •1.6.2.1. Sensitivities
- •1.6.2.2. Scatter Plots
- •1.6.2.3. Correlation Matrix
- •1.6.3. Generating an HTML Report
- •1.7. Multiple Probabilistic Design Executions
- •1.7.1. Saving the Probabilistic Design Database
- •1.7.2. Restarting a Probabilistic Design Analysis
- •1.7.3. Clearing the Probabilistic Design Database
- •1.8. Example Probabilistic Design Analysis
- •1.8.1. Problem Description
- •1.8.2. Problem Specifications
- •1.8.2.1. Problem Sketch
- •1.8.3. Using a Batch File for the Analysis
- •1.8.4. Using the GUI for the PDS Analysis
- •Chapter 2: Variational Technology
- •2.1. Harmonic Sweep Using VT Accelerator
- •2.1.1. Structural Elements Supporting Frequency-Dependent Properties
- •2.1.2. Harmonic Sweep for Structural Analysis with Frequency-Dependent Material Properties
- •2.1.2.1. Beam Example
- •Chapter 3: Adaptive Meshing
- •3.1. Prerequisites for Adaptive Meshing
- •3.2. Employing Adaptive Meshing
- •3.3. Modifying the Adaptive Meshing Process
- •3.3.1. Selective Adaptivity
- •3.3.2. Customizing the ADAPT Macro with User Subroutines
- •3.3.2.1. Creating a Custom Meshing Subroutine (ADAPTMSH.MAC)
- •3.3.2.2. Creating a Custom Subroutine for Boundary Conditions (ADAPTBC.MAC)
- •3.3.2.3. Creating a Custom Solution Subroutine (ADAPTSOL.MAC)
- •3.3.2.4. Some Further Comments on Custom Subroutines
- •3.3.3. Customizing the ADAPT Macro (UADAPT.MAC)
- •3.4. Adaptive Meshing Hints and Comments
- •3.5. Where to Find Examples
- •Chapter 4: Rezoning
- •4.1. Benefits and Limitations of Rezoning
- •4.1.1. Rezoning Limitations
- •4.2. Rezoning Requirements
- •4.3. Understanding the Rezoning Process
- •4.3.1. Overview of the Rezoning Process Flow
- •4.3.2. Key Commands Used in Rezoning
- •4.4. Step 1: Determine the Substep to Initiate Rezoning
- •4.5. Step 2. Initiate Rezoning
- •4.6. Step 3: Select a Region to Remesh
- •4.7. Step 4: Perform the Remeshing Operation
- •4.7.1. Choosing a Remeshing Method
- •4.7.1.1. Remeshing Using a Program-Generated New Mesh (2-D)
- •4.7.1.1.1. Creating an Area to Remesh
- •4.7.1.1.2. Using Nodes From the Old Mesh
- •4.7.1.1.3. Hints for Remeshing Multiple Regions
- •4.7.1.1.4. Generating a New Mesh
- •4.7.1.2. Remeshing Using a Generic New Mesh (2-D and 3-D)
- •4.7.1.2.1. Using the REMESH Command with a Generic New Mesh
- •4.7.1.2.2. Requirements for the Generic New Mesh
- •4.7.1.2.3. Using the REGE and KEEP Remeshing Options
- •4.7.1.3. Remeshing Using Manual Mesh Splitting (2-D and 3-D)
- •4.7.1.3.1. Understanding Mesh Splitting
- •4.7.1.3.2. Geometry Details for Mesh Splitting
- •4.7.1.3.3. Using the REMESH Command for Mesh Splitting
- •4.7.1.3.4. Mesh-Transition Options for 2-D Mesh Splitting
- •4.7.1.3.5. Mesh-Transition Options for 3-D Mesh Splitting
- •4.7.1.3.7. Improving Tetrahedral Element Quality via Mesh Morphing
- •4.7.2. Mesh Control
- •4.7.3. Remeshing Multiple Regions at the Same Substep
- •4.8. Step 5: Verify Applied Contact Boundaries, Surface-Effect Elements, Loads, and Boundary Conditions
- •4.8.1. Contact Boundaries
- •4.8.2. Surface-Effect Elements
- •4.8.3. Pressure and Contiguous Displacements
- •4.8.4. Forces and Isolated Applied Displacements
- •4.8.5. Nodal Temperatures
- •4.8.6. Other Boundary Conditions and Loads
- •4.9. Step 6: Automatically Map Variables and Balance Residuals
- •4.9.1. Mapping Solution Variables
- •4.9.2. Balancing Residual Forces
- •4.9.3. Interpreting Mapped Results
- •4.9.4. Handling Convergence Difficulties
- •4.10. Step 7: Perform a Multiframe Restart
- •4.11. Repeating the Rezoning Process if Necessary
- •4.11.1. File Structures for Repeated Rezonings
- •4.12. Postprocessing Rezoning Results
- •4.12.1. The Database Postprocessor
- •4.12.1.1. Listing the Rezoning Results File Summary
- •4.12.1.2. Animating the Rezoning Results
- •4.12.1.3. Using the Results Viewer for Rezoning
- •4.12.2. The Time-History Postprocessor
- •4.13. Rezoning Restrictions
- •4.14. Rezoning Examples
- •4.14.1. Example: Rezoning Using a Program-Generated New Mesh
- •4.14.1.1. Initial Input for the Analysis
- •4.14.1.2. Rezoning Input for the Analysis
- •4.14.2. Example: Rezoning Using a Generic New Mesh
- •4.14.2.1. Initial Input for the Analysis
- •4.14.2.2. Exporting the Distorted Mesh as a CDB File
- •4.14.2.3. Importing the File into ANSYS ICEM CFD and Generating a New Mesh
- •4.14.2.4. Rezoning Using the New CDB Mesh
- •Chapter 5: Mesh Nonlinear Adaptivity
- •5.1. Mesh Nonlinear Adaptivity Benefits, Limitations and Requirements
- •5.1.1. Rubber Seal Simulation
- •5.1.2. Crack Simulation
- •5.2. Understanding the Mesh Nonlinear Adaptivity Process
- •5.2.1. Checking Nonlinear Adaptivity Criteria
- •5.2.1.1. Defining Element Components
- •5.2.1.2. Defining Nonlinear Adaptivity Criteria
- •5.2.1.3. Defining Criteria-Checking Frequency
- •5.3. Mesh Nonlinear Adaptivity Criteria
- •5.3.1. Energy-Based
- •5.3.2. Position-Based
- •5.3.3. Contact-Based
- •5.3.4. Frequency of Criteria Checking
- •5.4. How a New Mesh Is Generated
- •5.5. Convergence at Substeps with the New Mesh
- •5.6. Controlling Mesh Nonlinear Adaptivity
- •5.7. Postprocessing Mesh Nonlinear Adaptivity Results
- •5.8. Mesh Nonlinear Adaptivity Examples
- •5.8.1. Example: Rubber Seal Simulation
- •5.8.2. Example: Crack Simulation
- •Chapter 6: 2-D to 3-D Analysis
- •6.1. Benefits of 2-D to 3-D Analysis
- •6.2. Requirements for a 2-D to 3-D Analysis
- •6.3. Overview of the 2-D to 3-D Analysis Process
- •6.3.1. Overview of the 2-D to 3-D Analysis Process Flow
- •6.3.2. Key Commands Used in 2-D to 3-D Analysis
- •6.4. Performing a 2-D to 3-D Analysis
- •6.4.1. Step 1: Determine the Substep to Initiate
- •6.4.2. Step 2: Initiate the 2-D to 3-D Analysis
- •6.4.3. Step 3: Extrude the 2-D Mesh to the New 3-D Mesh
- •6.4.4. Step 4: Map Solution Variables from 2-D to 3-D Mesh
- •6.4.5. Step 5: Perform an Initial-State-Based 3-D Analysis
- •6.5. 2-D to 3-D Analysis Restrictions
- •Chapter 7: Cyclic Symmetry Analysis
- •7.1. Understanding Cyclic Symmetry Analysis
- •7.1.1. How the Program Automates a Cyclic Symmetry Analysis
- •7.1.2. Commands Used in a Cyclic Symmetry Analysis
- •7.2. Cyclic Modeling
- •7.2.1. The Basic Sector
- •7.2.2. Edge Component Pairs
- •7.2.2.1. CYCOPT Auto Detection Tolerance Adjustments for Difficult Cases
- •7.2.2.2. Identical vs. Dissimilar Edge Node Patterns
- •7.2.2.3. Unmatched Nodes on Edge-Component Pairs
- •7.2.2.4. Identifying Matching Node Pairs
- •7.2.3. Modeling Limitations
- •7.2.4. Model Verification (Preprocessing)
- •7.3. Solving a Cyclic Symmetry Analysis
- •7.3.1. Understanding the Solution Architecture
- •7.3.1.1. The Duplicate Sector
- •7.3.1.2. Coupling and Constraint Equations (CEs)
- •7.3.1.3. Non-Cyclically Symmetric Loading
- •7.3.1.3.1. Specifying Non-Cyclic Loading
- •7.3.1.3.2. Commands Affected by Non-Cyclic Loading
- •7.3.1.3.3. Plotting and Listing Non-Cyclic Boundary Conditions
- •7.3.1.3.4. Graphically Picking Non-Cyclic Boundary Conditions
- •7.3.2. Solving a Static Cyclic Symmetry Analysis
- •7.3.3. Solving a Modal Cyclic Symmetry Analysis
- •7.3.3.1. Understanding Harmonic Index and Nodal Diameter
- •7.3.3.2. Solving a Stress-Free Modal Analysis
- •7.3.3.3. Solving a Prestressed Modal Analysis
- •7.3.3.4. Solving a Large-Deflection Prestressed Modal Analysis
- •7.3.3.4.1. Solving a Large-Deflection Prestressed Modal Analysis with VT Accelerator
- •7.3.4. Solving a Linear Buckling Cyclic Symmetry Analysis
- •7.3.5. Solving a Harmonic Cyclic Symmetry Analysis
- •7.3.5.1. Solving a Full Harmonic Cyclic Symmetry Analysis
- •7.3.5.1.1. Solving a Prestressed Full Harmonic Cyclic Symmetry Analysis
- •7.3.5.2. Solving a Mode-Superposition Harmonic Cyclic Symmetry Analysis
- •7.3.5.2.1. Perform a Static Cyclic Symmetry Analysis to Obtain the Prestressed State
- •7.3.5.2.2. Perform a Linear Perturbation Modal Cyclic Symmetry Analysis
- •7.3.5.2.3. Restart the Modal Analysis to Create the Desired Load Vector from Element Loads
- •7.3.5.2.4. Obtain the Mode-Superposition Harmonic Cyclic Symmetry Solution
- •7.3.5.2.5. Review the Results
- •7.3.6. Solving a Magnetic Cyclic Symmetry Analysis
- •7.3.7. Database Considerations After Obtaining the Solution
- •7.3.8. Model Verification (Solution)
- •7.4. Postprocessing a Cyclic Symmetry Analysis
- •7.4.1. General Considerations
- •7.4.1.1. Using the /CYCEXPAND Command
- •7.4.1.1.1. /CYCEXPAND Limitations
- •7.4.1.2. Result Coordinate System
- •7.4.2. Modal Solution
- •7.4.2.1. Real and Imaginary Solution Components
- •7.4.2.2. Expanding the Cyclic Symmetry Solution
- •7.4.2.3. Applying a Traveling Wave Animation to the Cyclic Model
- •7.4.2.4. Phase Sweep of Repeated Eigenvector Shapes
- •7.4.3. Static, Buckling, and Full Harmonic Solutions
- •7.4.4. Mode-Superposition Harmonic Solution
- •7.5. Example Modal Cyclic Symmetry Analysis
- •7.5.1. Problem Description
- •7.5.2. Problem Specifications
- •7.5.3. Input File for the Analysis
- •7.5.4. Analysis Steps
- •7.6. Example Buckling Cyclic Symmetry Analysis
- •7.6.1. Problem Description
- •7.6.2. Problem Specifications
- •7.6.3. Input File for the Analysis
- •7.6.4. Analysis Steps
- •7.6.5. Solve For Critical Strut Temperature at Load Factor = 1.0
- •7.7. Example Harmonic Cyclic Symmetry Analysis
- •7.7.1. Problem Description
- •7.7.2. Problem Specifications
- •7.7.3. Input File for the Analysis
- •7.7.4. Analysis Steps
- •7.8. Example Magnetic Cyclic Symmetry Analysis
- •7.8.1. Problem Description
- •7.8.2. Problem Specifications
- •7.8.3. Input file for the Analysis
- •Chapter 8: Rotating Structure Analysis
- •8.1. Understanding Rotating Structure Dynamics
- •8.2. Using a Stationary Reference Frame
- •8.2.1. Campbell Diagram
- •8.2.2. Harmonic Analysis for Unbalance or General Rotating Asynchronous Forces
- •8.2.3. Orbits
- •8.3. Using a Rotating Reference Frame
- •8.4. Choosing the Appropriate Reference Frame Option
- •8.5. Example Campbell Diagram Analysis
- •8.5.1. Problem Description
- •8.5.2. Problem Specifications
- •8.5.3. Input for the Analysis
- •8.5.4. Analysis Steps
- •8.6. Example Coriolis Analysis
- •8.6.1. Problem Description
- •8.6.2. Problem Specifications
- •8.6.3. Input for the Analysis
- •8.6.4. Analysis Steps
- •8.7. Example Unbalance Harmonic Analysis
- •8.7.1. Problem Description
- •8.7.2. Problem Specifications
- •8.7.3. Input for the Analysis
- •8.7.4. Analysis Steps
- •Chapter 9: Submodeling
- •9.1. Understanding Submodeling
- •9.1.1. Nonlinear Submodeling
- •9.2. Using Submodeling
- •9.2.1. Create and Analyze the Coarse Model
- •9.2.2. Create the Submodel
- •9.2.3. Perform Cut-Boundary Interpolation
- •9.2.4. Analyze the Submodel
- •9.3. Example Submodeling Analysis Input
- •9.3.1. Submodeling Analysis Input: No Load-History Dependency
- •9.3.2. Submodeling Analysis Input: Load-History Dependency
- •9.4. Shell-to-Solid Submodels
- •9.5. Where to Find Examples
- •Chapter 10: Substructuring
- •10.1. Benefits of Substructuring
- •10.2. Using Substructuring
- •10.2.1. Step 1: Generation Pass (Creating the Superelement)
- •10.2.1.1. Building the Model
- •10.2.1.2. Applying Loads and Creating the Superelement Matrices
- •10.2.1.2.1. Applicable Loads in a Substructure Analysis
- •10.2.2. Step 2: Use Pass (Using the Superelement)
- •10.2.2.1. Clear the Database and Specify a New Jobname
- •10.2.2.2. Build the Model
- •10.2.2.3. Apply Loads and Obtain the Solution
- •10.2.3. Step 3: Expansion Pass (Expanding Results Within the Superelement)
- •10.3. Sample Analysis Input
- •10.4. Top-Down Substructuring
- •10.5. Automatically Generating Superelements
- •10.6. Nested Superelements
- •10.7. Prestressed Substructures
- •10.7.1. Static Analysis Prestress
- •10.7.2. Substructuring Analysis Prestress
- •10.8. Where to Find Examples
- •Chapter 11: Component Mode Synthesis
- •11.1. Understanding Component Mode Synthesis
- •11.1.1. CMS Methods Supported
- •11.1.2. Solvers Used in Component Mode Synthesis
- •11.2. Using Component Mode Synthesis
- •11.2.1. The CMS Generation Pass: Creating the Superelement
- •11.2.2. The CMS Use and Expansion Passes
- •11.2.3. Superelement Expansion in Transformed Locations
- •11.2.4. Plotting or Printing Mode Shapes
- •11.3. Example Component Mode Synthesis Analysis
- •11.3.1. Problem Description
- •11.3.2. Problem Specifications
- •11.3.3. Input for the Analysis: Fixed-Interface Method
- •11.3.4. Analysis Steps: Fixed-Interface Method
- •11.3.5. Input for the Analysis: Free-Interface Method
- •11.3.6. Analysis Steps: Free-Interface Method
- •11.3.7. Input for the Analysis: Residual-Flexible Free-Interface Method
- •11.3.8. Analysis Steps: Residual-Flexible Free-Interface Method
- •11.3.9. Example: Superelement Expansion in a Transformed Location
- •11.3.9.1. Analysis Steps: Superelement Expansion in a Transformed Location
- •11.3.10. Example: Reduce the Damping Matrix and Compare Full and CMS Results with RSTMAC
- •Chapter 12: Rigid-Body Dynamics and the ANSYS-ADAMS Interface
- •12.1. Understanding the ANSYS-ADAMS Interface
- •12.2. Building the Model
- •12.3. Modeling Interface Points
- •12.4. Exporting to ADAMS
- •12.4.1. Exporting to ADAMS via Batch Mode
- •12.4.2. Verifying the Results
- •12.5. Running the ADAMS Simulation
- •12.6. Transferring Loads from ADAMS
- •12.6.1. Transferring Loads on a Rigid Body
- •12.6.1.1. Exporting Loads in ADAMS
- •12.6.1.2. Importing Loads
- •12.6.1.3. Importing Loads via Commands
- •12.6.1.4. Reviewing the Results
- •12.6.2. Transferring the Loads of a Flexible Body
- •12.7. Methodology Behind the ANSYS-ADAMS Interface
- •12.7.1. The Modal Neutral File
- •12.7.2. Adding Weak Springs
- •12.8. Example Rigid-Body Dynamic Analysis
- •12.8.1. Problem Description
- •12.8.2. Problem Specifications
- •12.8.3. Command Input
- •Chapter 13: Element Birth and Death
- •13.1. Elements Supporting Birth and Death
- •13.2. Understanding Element Birth and Death
- •13.3. Element Birth and Death Usage Hints
- •13.3.1. Changing Material Properties
- •13.4. Using Birth and Death
- •13.4.1. Build the Model
- •13.4.2. Apply Loads and Obtain the Solution
- •13.4.2.1. Define the First Load Step
- •13.4.2.1.1. Sample Input for First Load Step
- •13.4.2.2. Define Subsequent Load Steps
- •13.4.2.2.1. Sample Input for Subsequent Load Steps
- •13.4.3. Review the Results
- •13.4.4. Use Analysis Results to Control Birth and Death
- •13.4.4.1. Sample Input for Deactivating Elements
- •13.5. Where to Find Examples
- •Chapter 14: User-Programmable Features and Nonstandard Uses
- •14.1. User-Programmable Features (UPFs)
- •14.1.1. Understanding UPFs
- •14.1.2. Types of UPFs Available
- •14.2. Nonstandard Uses of the ANSYS Program
- •14.2.1. What Are Nonstandard Uses?
- •14.2.2. Hints for Nonstandard Use of ANSYS
- •Chapter 15: State-Space Matrices Export
- •15.1. State-Space Matrices Based on Modal Analysis
- •15.1.1. Examples of SPMWRITE Command Usage
- •15.1.2. Example of Reduced Model Generation in ANSYS and Usage in Simplorer
- •15.1.2.1. Problem Description
- •15.1.2.2. Problem Specifications
- •15.1.2.3. Input File for the Analysis
- •Chapter 16: Soil-Pile-Structure Analysis
- •16.1. Soil-Pile-Structure Interaction Analysis
- •16.1.1. Automatic Pile Subdivision
- •16.1.2. Convergence Criteria
- •16.1.3. Soil Representation
- •16.1.4. Mudslides
- •16.1.5. Soil-Pile Interaction Results
- •16.1.5.1. Displacements and Reactions
- •16.1.5.2. Forces and Stresses
- •16.1.5.3. UNITY Check Data
- •16.2. Soil Data Definition and Examples
- •16.2.1. Soil Profile Data Definition
- •16.2.1.1. Mudline Position Definition
- •16.2.1.2. Common Factors for P-Y, T-Z Curves
- •16.2.1.3. Horizontal Soil Properties (P-Y)
- •16.2.1.3.1. P-Y curves defined explicitly
- •16.2.1.3.2. P-Y curves generated from given soil properties
- •16.2.1.4. Vertical Soil Properties (T-Z)
- •16.2.1.4.1. T-Z curves defined explicitly
- •16.2.1.4.2. T-Z curves generated from given soil properties
- •16.2.1.5. End Bearing Properties (ENDB)
- •16.2.1.5.1. ENDB curve defined explicitly
- •16.2.1.5.2. ENDB curves generated from given soil properties
- •16.2.1.6. Mudslide Definition
- •16.2.2. Soil Data File Examples
- •16.2.2.1. Example 1: Constant Linear Soil
- •16.2.2.2. Example 2: Non-Linear Soil
- •16.2.2.3. Example 3: Soil Properties Defined in 5 Layers
- •16.2.2.4. Example 4: Soil Properties Defined in 5 Layers with Mudslide
- •16.3. Performing a Soil-Pile Interaction Analysis
- •16.3.2. Mechanical APDL Component System Example
- •16.3.3. Static Structural Component System Example
- •16.4. Soil-Pile-Structure Results
- •16.5. References
- •Chapter 17: Coupling to External Aeroelastic Analysis of Wind Turbines
- •17.1. Sequential Coupled Wind Turbine Solution in Mechanical APDL
- •17.1.1. Procedure for a Sequentially Coupled Wind Turbine Analysis
- •17.1.2. Output from the OUTAERO Command
- •Chapter 18: Applying Ocean Loading from a Hydrodynamic Analysis
- •18.1. How Hydrodynamic Analysis Data Is Used
- •18.2. Hydrodynamic Load Transfer with Forward Speed
- •18.3. Hydrodynamic Data File Format
- •18.3.1. Comment (Optional)
- •18.3.2. General Model Data
- •18.3.3. Hydrodynamic Surface Geometry
- •18.3.4. Wave Periods
- •18.3.5. Wave Directions
- •18.3.6. Panel Pressures
- •18.3.7. Morison Element Hydrodynamic Definition
- •18.3.8. Morison Element Wave Kinematics Definition
- •18.3.9. RAO Definition
- •18.3.10. Mass Properties
- •18.4. Example Analysis Using Results from a Hydrodynamic Diffraction Analysis
- •Index
- •ОГЛАВЛЕНИЕ
- •ВВЕДЕНИЕ
- •1.1. Методология проектирования технологических объектов
- •1.2. Компьютерные технологии проектирования
- •1.3. Системы автоматизированного проектирования в технике
- •1.4. Системы инженерного анализа
- •2.2.1. Создание и сохранение чертежа
- •2.2.2. Изменение параметров чертежа
- •2.2.3. Заполнение основной надписи
- •2.2.4. Создание нового вида. Локальная система координат
- •2.2.5. Вычерчивание изображения прокладки
- •2.2.6. Простановка размеров
- •2.2.7. Ввод технических требований
- •2.2.8. Задание материала изделия
- •2.3. Сложные разрезы в чертеже детали «Основание»
- •2.3.1. Подготовка чертежа
- •Cохранить документ.
- •2.3.2. Черчение по сетке из вспомогательных линий
- •2.3.3. Изображение разрезов
- •2.4. Чертежи общего вида при проектировании
- •3.1. Интерфейс программы
- •3.2. Общее представление о трехмерном моделировании
- •3.3. Основные операции геометрического моделирования
- •3.3.1. Операция выдавливания
- •3.3.2. Операция вращения
- •3.3.3. Кинематическая операция
- •3.3.4. Построение тела по сечениям
- •3.4. Операции конструирования
- •3.4.1. Построение фасок и скруглений
- •3.4.2. Построение уклона
- •3.4.3. Сечение модели плоскостью
- •3.4.4. Сечение по эскизу
- •3.4.5. Создание моделей-сборок
- •3.5. Разработка электронных 3D-моделей тепловых устройств
- •3.5.1. Электронные модели в ЕСКД
- •3.5.2. Электронные «чертежи» в ЕСКД
- •3.5.4. Электронная модель сборочного изделия «Газовая горелка»
- •ГЛАВА 4. ИНЖЕНЕРНЫЙ АНАЛИЗ ГАЗОДИНАМИКИ И ТЕПЛООБМЕНА В ANSYS CFX
- •4.1. Область применения ANSYS CFX
- •4.2. Особенности вычислительного процесса в ANSYS CFX
- •4.3. Программы, используемые при расчетах в ANSYS CFX
- •4.4. Организация процесса вычислений в среде пакета Workbench
- •4.4.1. Графический интерфейс пользователя
- •5.1. Постановка теплофизических задач в ANSYS Multiphysics
- •5.2. Решение задач в пакете ANSYS Multiphysics
- •5.2.1. Графический интерфейс пользователя
- •5.2.2. Этапы препроцессорной подготовки решения
- •5.2.3. Этап получения решения и постпроцессорной обработки результатов
- •5.3.5. Нестационарный теплообмен. Нагрев пластины в печи с жидким теплоносителем
- •5.4.1. Температурные напряжения при нагреве
- •БИБЛИОГРАФИЧЕСКИЙ СПИСОК
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1.6.1.4. Print Probabilities
The PDS offers a function where you can determine the cumulative distribution function at any point along the axis of the probabilistic design variable, including an interpolation function so you can evaluate the probabilities between sampling points. This feature is most helpful if you want to evaluate the failure probability or reliability of your component for a very specific and given limit value.
To print probabilities:
Command(s): PDPROB
GUI: Main Menu> Prob Design> Prob Results> Statistics> Probabilities
1.6.1.5. Print Inverse Probabilities
The PDS offers a function where you can probe the cumulative distribution function by specifying a certain probability level; the PDS tells you at which value of the probabilistic design variable this probability will occur. This is helpful if you want to evaluate what limit you should specify to not exceed a certain failure probability, or to specifically achieve a certain reliability for your component.
To print inverse probabilities:
Command(s): PDPINV
GUI: Main Menu> Prob Design> Prob Results> Statistics> Inverse Prob
1.6.2. Trend Postprocessing
Trend postprocessing allows you several options for reviewing your results.
1.6.2.1. Sensitivities
Probabilistic sensitivities are important in allowing you to improve your design toward a more reliable and better quality product, or to save money while maintaining the reliability or quality of your product. You can request a sensitivity plot for any random output parameter in your model.
There is a difference between probabilistic sensitivities and deterministic sensitivities. Deterministic sensitivities are mostly only local gradient information. For example, to evaluate deterministic sensitivities you can vary each input parameters by ±10% (one at a time) while keeping all other input parameters constant, then see how the output parameters react to these variations. As illustrated in the figure below, an output parameter would be considered very sensitive with respect to a certain input parameter if you observe a large change of the output parameter value.
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Figure 1.12: Sensitivities
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These purely deterministic considerations have various disadvantages that are taken into consideration for probabilistic sensitivities, namely:
•A deterministic variation of an input parameter that is used to determine the gradient usually does not take the physical range of variability into account. An input parameter varied by ±10% is not meaningful for the analysis if ±10% is too much or too little compared with the actual range of physical variability and randomness. In a probabilistic approach the physical range of variability is inherently considered because of the distribution functions for input parameters. Probabilistic sensitivities measure how much the range of scatter of an output parameter is influenced by the scatter of the random input variables. Hence, two effects have an influence on probabilistic sensitivities: the slope of the gradient, plus the width of the scatter range of the random input variables. This is illustrated in the figures below. If a random input variable has a certain given range of scatter, then the scatter of the corresponding random output parameter is larger, and the larger the slope of the
output parameter curve is (first illustration). But remember that an output parameter with a moderate slope can have a significant scatter if the random input variables have a wider range of scatter (second illustration).
Figure 1.13: Range of Scatter
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•Gradient information is local information only. It does not take into account that the output parameter may react more or less with respect to variation of input parameters at other locations in the input parameter space. However, the probabilistic approach not only takes the slope at a particular location into account, but also all the values the random output parameter can have within the space of the random input variables.
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•Deterministic sensitivities are typically evaluated using a finite-differencing scheme (varying one parameter at a time while keeping all others fixed). This neglects the effect of interactions between input parameters. An interaction between input parameters exists if the variation of a certain parameter has a greater or lesser effect if at the same time one or more other input parameters change their values as well. In some cases interactions play an important or even dominant role. This is the case if an input parameter is not significant on its own but only in connection with at least one other input parameter. Generally, interactions play an important role in 10% to 15% of typical engineering analysis cases (this figure is problem dependent). If interactions are important, then a deterministic sensitivity analysis can give you completely incorrect results. However, in a probabilistic approach, the results are always based on Monte Carlo simulations, either directly performed using you analysis
model or using response surface equations. Inherently, Monte Carlo simulations always vary all random input variables at the same time; thus if interactions exist then they will always be correctly reflected in the probabilistic sensitivities.
To display sensitivities, the PDS first groups the random input variables into two groups: those having a significant influence on a particular random output parameter and those that are rather insignificant,
based on a statistical significance test. This tests the hypothesis that the sensitivity of a particular random input variable is identical to zero and then calculates the probability that this hypothesis is true. If the probability exceeds a certain significance level (determining that the hypothesis is likely to be true),
then the sensitivity of that random input variable is negligible. The PDS will plot only the sensitivities of the random input variables that are found to be significant. However, insignificant sensitivities are printed in the output window. You can also review the significance probabilities used by the hypothesis test to decide which group a particular random input variable belonged to.
The PDS allows you to visualize sensitivities either as a bar chart, a pie chart, or both. Sensitivities are ranked so the random input variable having the highest sensitivity appears first.
In a bar chart the most important random input variable (with the highest sensitivity) appears in the leftmost position and the others follow to the right in the order of their importance. A bar chart describes the sensitivities in an absolute fashion (taking the signs into account); a positive sensitivity indicates
that increasing the value of the random input variable increases the value of the random output parameter for which the sensitivities are plotted. Likewise, a negative sensitivity indicates that increasing the random input variable value reduces the random output parameter value. In a pie chart, sensitivities are relative to each other.
In a pie chart the most important random input variable (with the highest sensitivity) will appear first after the 12 o'clock position, and the others follow in clockwise direction in the order of their importance.
Using a sensitivity plot, you can answer the following important questions.
How can I make the component more reliable or improve its quality?
If the results for the reliability or failure probability of the component do not reach the expected levels, or if the scatter of an output parameter is too wide and therefore not robust enough for a quality product, then you should make changes to the important input variables first. Modifying an input variable that is insignificant would be waste of time.
Of course you are not in control of all random input parameters. A typical example where you have very limited means of control are material properties. For example, if it turns out that the environmental temperature (outdoor) is the most important input parameter then there is probably nothing you can do. However, even if you find out that the reliability or quality of your product is driven by parameters that you cannot control, this has importance — it is likely that you have a fundamental flaw in your product design! You should watch for influential parameters like these.
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If the input variable you want to tackle is a geometry-related parameter or a geometric tolerance, then improving the reliability and quality of your product means that it might be necessary to change to a more accurate manufacturing process or use a more accurate manufacturing machine. If it is a material property, then there is might be nothing you can do about it. However, if you only had a few measurements for a material property and consequently used only a rough guess about its scatter and the material property turns out to be an important driver of product reliability and quality, then it makes sense to collect more raw data. In this way the results of a probabilistic analysis can help you spend your money where it makes the most sense — in areas that affect the reliability and quality of your products the most.
How can I save money without sacrificing the reliability or the quality of the product?
If the results for the reliability or failure probability of the component are acceptable or if the scatter of an output parameter is small and therefore robust enough for a quality product then there is usually the question of how to save money without reducing the reliability or quality. In this case, you should first make changes to the input variables that turned out to be insignificant, because they do not effect
the reliability or quality of your product. If it is the geometrical properties or tolerances that are insignificant, you can consider applying a less expensive manufacturing process. If a material property turns out to be insignificant, then this is not typically a good way to save money, because you are usually not in control of individual material properties. However, the loads or boundary conditions can be a potential for saving money, but in which sense this can be exploited is highly problem dependent.
Command(s): PDSENS
GUI: Main Menu> Prob Design> Prob Results> Trends> Sensitivities
1.6.2.2. Scatter Plots
While the sensitivities point indicate which probabilistic design parameters you need to modify to have an impact on the reliability or failure probability, scatter plots give you a better understanding of how and how far you should modify the input variables. Improving the reliability and quality of a product typically means that the scatter of the relevant random output parameters must be reduced.
The PDS allows you to request a scatter plot of any probabilistic design variable versus any other one,
so you can visualize the relationship between two design variables (input variables or output parameters). This allows you to verify that the sample points really show the pattern of correlation that you specified (if you did so). Typically, random output parameters are correlated as because they are generated by
the same set of random input variables. To support the process of improving the reliability or quality
of your product, a scatter plot showing a random output parameter as a function of the most important random input variable can be very helpful.
When you display a scatter plot, the PDS plots the sampling points and a trendline. For this trendline, the PDS uses a polynomial function and lets you chose the order of the polynomial function. If you plot a random output parameter as a function of a random input variable, then this trendline expresses how much of the scatter on the random output parameter (Y-axis) is controlled by the random input variable (X-axis). The deviations of the sample points from the trendline are caused and controlled by all the other random input variables. If you want to reduce the scatter of the random output parameter to improve reliability and quality, you have two options:
•Reduce the width of the scatter of the most important random input variable(s) (that you have control over).
•Shift the range of the scatter of the most important random input variable(s) (that you have control over).
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The effect of reducing and shifting the scatter of a random input variable is illustrated in the figures below. "Input range before" denotes the scatter range of the random input variable before the reduction or shifting, and "input range after" illustrates how the scatter range of the random input variable has been modified. In both cases, the trendline tells how much the scatter of the output parameter is affected and in which way if the range of scatter of the random input variable is modified.
Figure 1.14: Effects of Reducing and Shifting Range of Scatter
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It depends on your particular problem if either reducing or shifting the range of scatter of a random input variable is preferable. In general, reducing the range of scatter of a random input variable leads to higher costs. A reduction of the scatter range requires a more accurate process in manufacturing or operating the product — the more accurate, the more expensive it is. This might lead you to conclude that shifting the scatter range is a better idea, because it preserves the width of the scatter (which means you can still use the manufacturing or operation process that you have). Below are some considerations if you want to do that:
•Shifting the scatter range of a random input variable can only lead to a reduction of the scatter of a random output parameter if the trendline shows a clear nonlinearity. If the trendline indicates a linear trend (if it is a straight line), then shifting the range of the input variables anywhere along this straight line doesn't make any difference. For this, reducing the scatter range of the random input variable remains your only option.
•It is obvious from the second illustration above that shifting the range of scatter of the random input variable involves an extrapolation beyond the range where you have data. Extrapolation is always difficult and even dangerous if done without care. The more sampling points the trendline is based on the better you can extrapolate. Generally, you should not go more than 30-40% outside of the range of your data. But the advantage of focusing on the important random input variables is that
a slight and careful modification can make a difference.
Note
ANSYS strongly recommends that you redo the entire probabilistic analysis using the new and modified random input variables if you have reduced of shifted the scatter range of any input variables using the procedure and recommendations above. To redo the probabilistic analysis, save the probabilistic model using the PDSAVE command and clear the current probabilistic analysis results using the PDCLR,POST command. Then you can modify the random input variable definitions and redo the probabilistic analysis.
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