Curriculum
- 9 Sections
- 210 Lessons
- 10 Weeks
Expand all sectionsCollapse all sections
- Module 1: OrcaFlex Python API and Environment Setup17
- 1.1Introduction to OrcaFlex automation
- 1.2Benefits of Python-driven engineering workflows
- 1.3Manual workflows versus automated workflows
- 1.4Introduction to OrcFxAPI
- 1.5Understanding OrcaFlex–Python interaction
- 1.6Python installation and environment configuration
- 1.7Installing and importing required Python libraries
- 1.8Importing OrcFxAPI
- 1.9Testing OrcaFlex connectivity through Python
- 1.10Understanding the OrcFxAPI object structure
- 1.11Working with Model objects
- 1.12Accessing OrcaFlex objects through Python
- 1.13Understanding properties, methods, and API calls
- 1.14Basic Python scripting patterns for OrcaFlex
- 1.15Working with variables, loops, functions, lists, and dictionaries
- 1.16File handling concepts for automation scripts
- 1.17Running a basic OrcaFlex Python script
- Module 2: Programmatic Model Creation and Object Management28
- 2.1Creating a new OrcaFlex model using Python
- 2.2Understanding model initialization
- 2.3Creating model objects programmatically
- 2.4Working with:
- 2.5Lines
- 2.6Vessels
- 2.7Buoys
- 2.8Anchors and connections
- 2.9Line types
- 2.10Shapes and other model components
- 2.11Naming and identifying model objects
- 2.12Setting object properties through scripts
- 2.13Configuring environmental conditions
- 2.14Defining water depth
- 2.15Setting wave conditions
- 2.16Configuring current parameters
- 2.17Configuring wind conditions where applicable
- 2.18Working with line properties
- 2.19Assigning line types and segment information
- 2.20Managing connections and coordinates
- 2.21Iterating through model objects
- 2.22Searching and filtering objects
- 2.23Bulk object manipulation
- 2.24Creating reusable Python functions
- 2.25Deleting and recreating objects programmatically
- 2.26Saving generated OrcaFlex models
- 2.27Applying automated file naming conventions
- 2.28Practical Exercise Create an OrcaFlex model through Python, configure key objects and environmental conditions, and save the generated model automatically.
- Module 3: Model Modification, Batch Processing and Parametric Studies26
- 3.1Loading existing OrcaFlex models through Python
- 3.2Reading current model configurations
- 3.3Accessing object properties
- 3.4Changing model parameters programmatically
- 3.5Updating line characteristics
- 3.6Modifying vessel properties
- 3.7Changing environmental conditions
- 3.8Updating object positions and connections
- 3.9Applying parameter changes across several objects
- 3.10Bulk modification techniques
- 3.11Automating repetitive model changes
- 3.12Creating model variants
- 3.13Parameter-driven model generation
- 3.14Introduction to batch processing
- 3.15Processing multiple OrcaFlex model files
- 3.16Folder and directory traversal
- 3.17Automatically identifying .dat and .sim files
- 3.18Creating parameter combinations
- 3.19Introduction to parametric studies
- 3.20Automating environmental variation studies
- 3.21Automating geometry and configuration variations
- 3.22Sensitivity analysis workflows
- 3.23Maintaining traceability between model variations
- 3.24Automated model naming and storage
- 3.25Practical Exercise
- 3.26Generate multiple OrcaFlex model variants by changing environmental and structural parameters using a single Python automation script.
- Module 4: Automated Simulation Execution and Management23
- 4.1Understanding simulation control through OrcFxAPI
- 4.2Running statics programmatically
- 4.3Running dynamic simulations through Python
- 4.4Checking simulation states
- 4.5Monitoring simulation progress
- 4.6Saving simulation results
- 4.7Managing .sim files automatically
- 4.8Executing multiple simulation cases
- 4.9Sequential batch simulation
- 4.10Automating simulation folders
- 4.11Detecting completed and failed simulations
- 4.12Handling interrupted simulation processes
- 4.13Managing simulation exceptions
- 4.14Restart and recovery concepts
- 4.15Recording simulation status
- 4.16Creating execution logs
- 4.17Managing large simulation sets
- 4.18Introduction to parallel processing
- 4.19Multiprocessing versus multithreading concepts
- 4.20Considerations for OrcaFlex licensing and computing resources
- 4.21Improving efficiency of automated simulation workflows
- 4.22Practical Exercise
- 4.23Execute several model cases automatically, track simulation status, save completed simulations, and record unsuccessful cases for review.
- Module 5: Automated Results Extraction and Post-Processing30
- 5.1Introduction to OrcaFlex results access through Python
- 5.2Accessing simulation results
- 5.3Understanding result variables
- 5.4Retrieving time history results
- 5.5Selecting analysis periods
- 5.6Extracting results for individual model objects
- 5.7Accessing line results
- 5.8Retrieving vessel response data
- 5.9Extracting buoy and connection results
- 5.10Accessing range graph results
- 5.11Obtaining statistics from simulations
- 5.12Extracting:
- 5.13Maximum values
- 5.14Minimum values
- 5.15Mean values
- 5.16Standard deviations
- 5.17Extreme responses
- 5.18Extracting tension results
- 5.19Motion and displacement results
- 5.20Load and force extraction
- 5.21Engineering result calculations
- 5.22Processing multiple result variables automatically
- 5.23Comparing results between simulation cases
- 5.24Working with Python data structures for result processing
- 5.25Introduction to NumPy and pandas for engineering data
- 5.26Organizing extracted simulation results
- 5.27Filtering and transforming simulation data
- 5.28Automated post-processing workflows
- 5.29Practical Exercise
- 5.30Extract selected engineering results from multiple OrcaFlex simulations and consolidate them into a structured dataset.
- Module 6: External Data Integration and Automated Reporting22
- 6.1Integrating OrcaFlex automation with external datasets
- 6.2Reading input data from CSV files
- 6.3Reading model parameters from Excel
- 6.4Using pandas for input management
- 6.5Mapping external parameters to OrcaFlex properties
- 6.6Creating models from tabulated input data
- 6.7Automating model configurations from engineering datasets
- 6.8Writing results to CSV
- 6.9Exporting processed results to Excel
- 6.10Creating structured result summaries
- 6.11Generating comparison tables
- 6.12Automated plotting using Python
- 6.13Creating engineering result graphs
- 6.14Organizing output directories
- 6.15Automated file and folder management
- 6.16Naming models and result files systematically
- 6.17Generating summary reports
- 6.18Combining model inputs and simulation outputs
- 6.19Creating repeatable reporting workflows
- 6.20Maintaining traceability of simulation cases
- 6.21Practical Exercise
- 6.22Read model parameters from an external file, execute OrcaFlex cases, collect simulation results, and automatically generate a consolidated result report.
- Module 7: Advanced Python Automation Techniques in OrcaFlex29
- 7.1Designing reusable OrcaFlex automation scripts
- 7.2Modular Python script organization
- 7.3Functions and reusable automation components
- 7.4Configuration-driven automation
- 7.5Working with larger parameter sets
- 7.6Advanced batch processing
- 7.7Parallel simulation concepts
- 7.8Multiprocessing techniques
- 7.9Multithreading considerations
- 7.10Managing computational resources
- 7.11Introduction to Python External Functions
- 7.12Using Python functions within OrcaFlex workflows
- 7.13Passing information between OrcaFlex and Python
- 7.14Custom engineering calculations
- 7.15Integration with external Python libraries
- 7.16Automated validation of model inputs
- 7.17Model integrity checks
- 7.18Result validation routines
- 7.19Python exception handling
- 7.20try, except, and finally concepts
- 7.21Managing OrcFxAPI exceptions
- 7.22Logging automation activities
- 7.23Recording errors and warnings
- 7.24Creating recovery mechanisms
- 7.25Improving script maintainability
- 7.26Performance considerations for large automation projects
- 7.27Good practices for engineering automation scripts
- 7.28Practical Exercise
- 7.29Create a reusable automation framework incorporating model processing, validation, exception handling, logging, and automated result management.
- Module 8: Applied OrcaFlex Python Automation Case Studies34
- 8.1Case Study 1: Automated Model Generation
- 8.2Read model parameters
- 8.3Generate OrcaFlex objects
- 8.4Configure environmental conditions
- 8.5Save automatically generated models
- 8.6Case Study 2: Batch Simulation Automation
- 8.7Identify multiple model files
- 8.8Execute simulations automatically
- 8.9Monitor execution
- 8.10Save simulation outputs
- 8.11Log completed and failed cases
- 8.12Case Study 3: Parametric Environmental Study
- 8.13Define wave and current variations
- 8.14Generate multiple simulation combinations
- 8.15Execute cases
- 8.16Retrieve critical engineering responses
- 8.17Case Study 4: Sensitivity Analysis
- 8.18Select engineering parameters
- 8.19Automatically vary selected inputs
- 8.20Compare simulation outputs
- 8.21Identify influential parameters
- 8.22Case Study 5: Automated Result Extraction
- 8.23Process several .sim files
- 8.24Extract key response variables
- 8.25Calculate maximum and minimum values
- 8.26Consolidate engineering results
- 8.27Case Study 6: Automated Reporting Workflow
- 8.28Read input data
- 8.29Modify OrcaFlex models
- 8.30Execute simulations
- 8.31Extract results
- 8.32Process engineering metrics
- 8.33Export results to Excel/CSV
- 8.34Generate plots and summary tables
- Final Practical Workflow1
Generate plots and summary tables
Prev