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OrcaFlex Specialist Skill
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SKILL.md
| Name | orcaflex-specialist |
| Description | OrcaFlex Specialist Skill |
name: orcaflex-specialist version: "1.0.0" category: engineering description: "OrcaFlex Specialist Skill" capabilities: [] requires: [] see_also: []
OrcaFlex Specialist Skill
name: orcaflex-specialist
version: 1.0.0
category: sme
tags: [orcaflex, offshore, simulation, python-api, automation, marine-dynamics, mooring, riser]
created: 2026-01-06
updated: 2026-01-06
author: Claude
description: |
Expert OrcaFlex workflows, Python API automation, model validation, and best
practices for offshore marine simulations. Covers mooring analysis, riser
dynamics, installation simulations, and advanced post-processing.
When to Use This Skill
Use this skill when you need to:
- Automate OrcaFlex model creation and analysis
- Build parametric OrcaFlex models via Python API
- Perform batch simulations with varying parameters
- Extract and process time series results
- Validate OrcaFlex models against design criteria
- Integrate OrcaFlex with external tools and workflows
- Optimize mooring and riser configurations
- Conduct sensitivity studies and Monte Carlo simulations
Core Knowledge Areas
1. OrcaFlex Python API Basics
Connecting to OrcaFlex and basic model operations:
import OrcFxAPI
import numpy as np
from pathlib import Path
from typing import List, Dict, Optional
def create_new_model(
general_data: dict,
save_path: Path = None
) -> OrcFxAPI.Model:
"""
Create a new OrcaFlex model with general settings.
Args:
general_data: Dictionary with general data parameters
save_path: Optional path to save the model
Returns:
OrcaFlex model object
Example:
>>> general_data = {
... 'ImplicitUseVariableTimeStep': 'Yes',
... 'TargetLogSampleInterval': 0.1,
... 'InnerTimeStep': 0.01,
... 'StageCount': 2,
... 'StageDuration': [100, 3600]
... }
>>> model = create_new_model(general_data)
"""
# Create new model
model = OrcFxAPI.Model()
# Set general data
general = model.general
for key, value in general_data.items():
setattr(general, key, value)
# Save if path provided
if save_path:
model.SaveData(str(save_path))
return model
def load_model(
file_path: Path,
validate: bool = True
) -> OrcFxAPI.Model:
"""
Load existing OrcaFlex model with optional validation.
Args:
file_path: Path to .dat or .sim file
validate: Whether to validate model after loading
Returns:
Loaded OrcaFlex model
Example:
>>> model = load_model(Path('mooring_analysis.dat'))
>>> print(f"Model stages: {model.general.StageCount}")
"""
model = OrcFxAPI.Model(str(file_path))
if validate:
# Run basic validation
state = model.State()
if state != OrcFxAPI.ModelState.Reset:
print(f"Warning: Model state is {state}")
# Check for invalid objects
invalid_objects = []
for obj in model.objects:
try:
obj_type = obj.type
except:
invalid_objects.append(obj.name)
if invalid_objects:
print(f"Warning: Invalid objects found: {invalid_objects}")
return model
def run_static_analysis(
model: OrcFxAPI.Model,
thread_count: int = None
) -> None:
"""
Run static analysis (statics to whole simulation).
Args:
model: OrcaFlex model
thread_count: Number of threads (None = auto)
Example:
>>> model = load_model('mooring.dat')
>>> run_static_analysis(model, thread_count=4)
>>> print("Static analysis complete")
"""
# Configure calculation
if thread_count:
model.general.ThreadCount = thread_count
# Run statics to whole simulation
model.CalculateStatics()
model.RunSimulation()
print(f"Simulation complete. State: {model.State()}")
def run_dynamic_analysis(
model: OrcFxAPI.Model,
save_sim: bool = True,
sim_path: Path = None
) -> Path:
"""
Run dynamic analysis and save results.
Args:
model: OrcaFlex model
save_sim: Whether to save simulation results
sim_path: Path to save .sim file
Returns:
Path to saved simulation file
Example:
>>> model = load_model('mooring.dat')
>>> sim_file = run_dynamic_analysis(model, sim_path=Path('results.sim'))
>>> print(f"Results saved: {sim_file}")
"""
# Run simulation
model.RunSimulation()
# Save results
if save_sim:
if sim_path is None:
# Generate default path
sim_path = Path(model.DataFileName()).with_suffix('.sim')
model.SaveSimulation(str(sim_path))
return sim_path
return None
2. Building Models Programmatically
Create complex models via Python API:
def create_vessel_model(
vessel_params: dict,
mooring_config: dict,
environment: dict
) -> OrcFxAPI.Model:
"""
Create complete vessel model with moorings and environment.
Args:
vessel_params: Vessel properties
mooring_config: Mooring line configuration
environment: Environmental conditions
Returns:
Complete OrcaFlex model
Example:
>>> vessel_params = {
... 'name': 'FPSO',
... 'mass': 150000, # tonnes
... 'length': 300, # m
... 'draft': 20,
... 'draft_fore': 20,
... 'draft_aft': 20
... }
>>> mooring_config = {
... 'pattern': 'spread',
... 'line_count': 8,
... 'line_length': 1500,
... 'line_type': 'chain_wire_chain'
... }
>>> environment = {
... 'water_depth': 1200,
... 'current_speed': 1.0,
... 'wave_height': 5.0,
... 'wave_period': 12.0
... }
>>> model = create_vessel_model(vessel_params, mooring_config, environment)
"""
# Create new model
model = OrcFxAPI.Model()
# Set environment
env = model.environment
# Water depth
env.WaterDepth = environment['water_depth']
# Current profile
env.RefCurrentSpeed = environment['current_speed']
env.CurrentDepths = [0, -environment['water_depth']]
env.CurrentSpeeds = [environment['current_speed'], 0.5 * environment['current_speed']]
# Waves (JONSWAP spectrum)
env.WaveType = 'JONSWAP'
env.WaveHs = environment['wave_height']
env.WaveTp = environment['wave_period']
env.WaveGamma = 3.3
# Create vessel
vessel = model.CreateObject(OrcFxAPI.otVessel, vessel_params['name'])
# Set vessel properties
vessel.Length = vessel_params['length']
vessel.Draft = vessel_params['draft']
vessel.DraftAtRest = vessel_params['draft']
vessel.Mass = vessel_params['mass']
# Displacement check
rho_sw = 1025 # kg/m³
g = 9.81
displacement = vessel_params['mass'] * 1000 * g # N
print(f"Vessel displacement: {displacement/1e6:.1f} MN")
# Create mooring lines
create_mooring_system(
model=model,
vessel=vessel,
config=mooring_config,
water_depth=environment['water_depth']
)
return model
def create_mooring_system(
model: OrcFxAPI.Model,
vessel: OrcFxAPI.OrcaFlexObject,
config: dict,
water_depth: float
) -> List[OrcFxAPI.OrcaFlexObject]:
"""
Create mooring system attached to vessel.
Args:
model: OrcaFlex model
vessel: Vessel object
config: Mooring configuration
water_depth: Water depth [m]
Returns:
List of mooring line objects
Example:
>>> config = {
... 'pattern': 'spread',
... 'line_count': 8,
... 'line_length': 1500,
... 'fairlead_radius': 40,
... 'anchor_radius': 1400,
... 'line_sections': [
... {'type': 'R4 Studless Chain', 'length': 300},
... {'type': '76mm Wire', 'length': 900},
... {'type': 'R4 Studless Chain', 'length': 300}
... ]
... }
>>> lines = create_mooring_system(model, vessel, config, 1200)
"""
lines = []
line_count = config['line_count']
# Create line types if needed
for section in config['line_sections']:
line_type_name = section['type']
if not model.objects.Exists(line_type_name, OrcFxAPI.otLineType):
# Create line type (simplified - actual properties depend on type)
line_type = model.CreateObject(OrcFxAPI.otLineType, line_type_name)
# Set properties based on name
# (In practice, load from database or specify explicitly)
# Calculate line positions (evenly distributed)
for i in range(line_count):
angle = 360.0 / line_count * i
# Create line
line_name = f"Mooring_{i+1}"
line = model.CreateObject(OrcFxAPI.otLine, line_name)
# End A: Fairlead (on vessel)
line.EndAConnection = vessel.name
line.EndAAzimuth = angle
line.EndAX = config['fairlead_radius'] * np.cos(np.radians(angle))
line.EndAY = config['fairlead_radius'] * np.sin(np.radians(angle))
line.EndAZ = -vessel.Draft # At keel
# End B: Anchor
anchor_x = config['anchor_radius'] * np.cos(np.radians(angle))
anchor_y = config['anchor_radius'] * np.sin(np.radians(angle))
line.EndBConnection = 'Fixed'
line.EndBX = anchor_x
line.EndBY = anchor_y
line.EndBZ = -water_depth
# Set line sections
line.NumberOfSections = len(config['line_sections'])
for j, section in enumerate(config['line_sections']):
line.SectionIndex = j + 1
line.LineType[j] = section['type']
line.Length[j] = section['length']
lines.append(line)
return lines
def add_6d_buoy(
model: OrcFxAPI.Model,
line: OrcFxAPI.OrcaFlexObject,
arc_length: float,
buoy_params: dict
) -> OrcFxAPI.OrcaFlexObject:
"""
Add 6D buoy to mooring line.
Args:
model: OrcaFlex model
line: Line object to attach buoy to
arc_length: Arc length along line for attachment [m]
buoy_params: Buoy properties
Returns:
6D buoy object
Example:
>>> buoy_params = {
... 'name': 'Subsurface_Buoy',
... 'mass': 10, # tonnes
... 'volume': 12, # m³
... 'Cd': 1.2,
... 'Ca': 1.0
... }
>>> buoy = add_6d_buoy(model, mooring_line, 600, buoy_params)
"""
# Create 6D buoy
buoy = model.CreateObject(OrcFxAPI.ot6DBuoy, buoy_params['name'])
# Connection to line
buoy.Connection = line.name
buoy.ConnectionArcLength = arc_length
# Properties
buoy.Mass = buoy_params['mass']
buoy.Volume = buoy_params['volume']
# Drag and added mass
buoy.Cd = buoy_params.get('Cd', 1.0)
buoy.Ca = buoy_params.get('Ca', 1.0)
# Initial position (will be calculated during statics)
buoy.InitialPosition = 'Calculated from line'
return buoy
3. Results Extraction and Post-Processing
Extract time series and perform analysis:
def extract_time_series(
model: OrcFxAPI.Model,
object_name: str,
variable_name: str,
object_extra: OrcFxAPI.OrcaFlexObjectExtra = OrcFxAPI.oeEndA,
period: OrcFxAPI.SpecifiedPeriod = OrcFxAPI.SpecifiedPeriod(OrcFxAPI.pnWholeSimulation)
) -> tuple:
"""
Extract time series data from simulation results.
Args:
model: OrcaFlex model (with results)
object_name: Name of object
variable_name: Variable name (e.g., 'Effective Tension')
object_extra: Object extra (e.g., oeEndA, oeEndB)
period: Period specification
Returns:
Tuple of (time_array, values_array)
Example:
>>> model = OrcFxAPI.Model('results.sim')
>>> time, tension = extract_time_series(
... model,
... 'Mooring_1',
... 'Effective Tension',
... OrcFxAPI.oeEndA
... )
>>> print(f"Max tension: {np.max(tension):.1f} kN")
"""
# Get object
obj = model[object_name]
# Extract time series
time = obj.TimeHistory(
'Time',
period=period,
objectExtra=object_extra
)
values = obj.TimeHistory(
variable_name,
period=period,
objectExtra=object_extra
)
return np.array(time), np.array(values)
def calculate_statistics(
time: np.ndarray,
values: np.ndarray,
exclude_buildup: float = 100.0
) -> dict:
"""
Calculate statistical parameters from time series.
Args:
time: Time array [s]
values: Values array
exclude_buildup: Duration to exclude from start [s]
Returns:
Dictionary with statistics
Example:
>>> time, tension = extract_time_series(model, 'Mooring_1', 'Effective Tension')
>>> stats = calculate_statistics(time, tension, exclude_buildup=100)
>>> print(f"Mean: {stats['mean']:.1f} kN")
>>> print(f"Std: {stats['std']:.1f} kN")
>>> print(f"Max: {stats['max']:.1f} kN")
"""
# Exclude build-up period
mask = time >= exclude_buildup
values_trimmed = values[mask]
if len(values_trimmed) == 0:
raise ValueError("No data after excluding build-up period")
stats = {
'mean': np.mean(values_trimmed),
'std': np.std(values_trimmed),
'min': np.min(values_trimmed),
'max': np.max(values_trimmed),
'range': np.ptp(values_trimmed), # Peak-to-peak
'median': np.median(values_trimmed),
'p95': np.percentile(values_trimmed, 95),
'p99': np.percentile(values_trimmed, 99)
}
return stats
def extract_range_graph(
model: OrcFxAPI.Model,
object_name: str,
variable_name: str,
period: OrcFxAPI.SpecifiedPeriod = OrcFxAPI.SpecifiedPeriod(OrcFxAPI.pnWholeSimulation)
) -> tuple:
"""
Extract range graph data (values along line/riser).
Args:
model: OrcaFlex model with results
object_name: Line/riser name
variable_name: Variable (e.g., 'Effective Tension', 'Curvature')
period: Period specification
Returns:
Tuple of (arc_length, values)
Example:
>>> arc, tension = extract_range_graph(
... model,
... 'Riser_1',
... 'Effective Tension'
... )
>>> print(f"Tension at TDP: {tension[np.argmin(np.abs(arc-1500))]:.1f} kN")
"""
obj = model[object_name]
# Get range graph
arc_length = obj.RangeGraph(
'Arc Length',
period=period
)
values = obj.RangeGraph(
variable_name,
period=period
)
return np.array(arc_length), np.array(values)
def find_touchdown_point(
model: OrcFxAPI.Model,
line_name: str
) -> dict:
"""
Find touchdown point location and tension.
Args:
model: OrcaFlex model with results
line_name: Line/riser name
Returns:
Dictionary with TDP info
Example:
>>> tdp_info = find_touchdown_point(model, 'Riser_1')
>>> print(f"TDP arc length: {tdp_info['arc_length']:.1f} m")
>>> print(f"TDP tension: {tdp_info['tension']:.1f} kN")
"""
line = model[line_name]
# Get clearance along line
period = OrcFxAPI.SpecifiedPeriod(OrcFxAPI.pnWholeSimulation)
arc, clearance = extract_range_graph(model, line_name, 'Clearance', period)
# Find where clearance crosses zero
# TDP is where clearance = 0
tdp_idx = np.argmin(np.abs(clearance))
# Get tension and curvature at TDP
arc_tdp, tension = extract_range_graph(model, line_name, 'Effective Tension', period)
arc_curv, curvature = extract_range_graph(model, line_name, 'Curvature', period)
return {
'arc_length': arc[tdp_idx],
'clearance': clearance[tdp_idx],
'tension': tension[tdp_idx],
'curvature': curvature[tdp_idx]
}
4. Batch Simulation and Parametric Studies
Automate multiple simulation runs:
def batch_simulation(
base_model_path: Path,
parameter_sets: List[dict],
output_dir: Path,
parallel: bool = False
) -> List[dict]:
"""
Run batch simulations with varying parameters.
Args:
base_model_path: Path to base model .dat file
parameter_sets: List of parameter dictionaries
output_dir: Directory to save results
parallel: Whether to run in parallel (not implemented - OrcaFlex limitation)
Returns:
List of results dictionaries
Example:
>>> parameter_sets = [
... {'Hs': 5.0, 'Tp': 10.0, 'current': 1.0},
... {'Hs': 7.5, 'Tp': 12.0, 'current': 1.5},
... {'Hs': 10.0, 'Tp': 14.0, 'current': 2.0}
... ]
>>> results = batch_simulation(
... base_model_path=Path('base_model.dat'),
... parameter_sets=parameter_sets,
... output_dir=Path('batch_results')
... )
"""
output_dir.mkdir(parents=True, exist_ok=True)
results = []
for i, params in enumerate(parameter_sets):
print(f"Running simulation {i+1}/{len(parameter_sets)}")
print(f"Parameters: {params}")
# Load base model
model = OrcFxAPI.Model(str(base_model_path))
# Apply parameters
if 'Hs' in params:
model.environment.WaveHs = params['Hs']
if 'Tp' in params:
model.environment.WaveTp = params['Tp']
if 'current' in params:
model.environment.RefCurrentSpeed = params['current']
# Run simulation
try:
model.RunSimulation()
# Save results
sim_path = output_dir / f"run_{i+1:03d}.sim"
model.SaveSimulation(str(sim_path))
# Extract key results
result = {
'run_id': i + 1,
'parameters': params,
'sim_file': sim_path,
'status': 'SUCCESS'
}
# Extract mooring tensions (example)
mooring_results = {}
for mooring_name in ['Mooring_1', 'Mooring_2', 'Mooring_3']:
if model.objects.Exists(mooring_name):
time, tension = extract_time_series(
model,
mooring_name,
'Effective Tension',
OrcFxAPI.oeEndA
)
stats = calculate_statistics(time, tension)
mooring_results[mooring_name] = stats
result['mooring_tensions'] = mooring_results
except Exception as e:
result = {
'run_id': i + 1,
'parameters': params,
'status': 'FAILED',
'error': str(e)
}
results.append(result)
return results
def parametric_study_mooring_pretension(
base_model: OrcFxAPI.Model,
line_name: str,
pretension_range: np.ndarray,
output_dir: Path
) -> dict:
"""
Parametric study varying mooring line pretension.
Args:
base_model: Base OrcaFlex model
line_name: Mooring line name
pretension_range: Array of pretension values to test [kN]
output_dir: Output directory
Returns:
Dictionary with results for each pretension
Example:
>>> model = load_model('mooring.dat')
>>> pretensions = np.linspace(500, 2000, 10)
>>> results = parametric_study_mooring_pretension(
... model,
... 'Mooring_1',
... pretensions,
... Path('pretension_study')
... )
"""
output_dir.mkdir(parents=True, exist_ok=True)
results = {}
for pretension in pretension_range:
print(f"Testing pretension: {pretension:.1f} kN")
# Create model copy
model = OrcFxAPI.Model()
model.LoadData(base_model.DataFileName())
# Set pretension
line = model[line_name]
line.WinchPayoutControlMode = 'Specified Pretension'
line.Pretension = pretension
# Run static analysis
model.CalculateStatics()
# Extract static results
line_after_statics = model[line_name]
static_tension_a = line_after_statics.StaticResult(
'Effective Tension',
objectExtra=OrcFxAPI.oeEndA
)
static_tension_b = line_after_statics.StaticResult(
'Effective Tension',
objectExtra=OrcFxAPI.oeEndB
)
# Run dynamic simulation
model.RunSimulation()
# Extract dynamic results
time, tension_a = extract_time_series(
model,
line_name,
'Effective Tension',
OrcFxAPI.oeEndA
)
stats_a = calculate_statistics(time, tension_a)
results[pretension] = {
'static_tension_a': static_tension_a,
'static_tension_b': static_tension_b,
'dynamic_stats': stats_a
}
return results
5. Model Validation and QA
Automated model checking:
def validate_model(
model: OrcFxAPI.Model,
checks: List[str] = None
) -> dict:
"""
Comprehensive model validation checks.
Args:
model: OrcaFlex model
checks: List of checks to perform (None = all)
Returns:
Dictionary with validation results
Example:
>>> model = load_model('mooring.dat')
>>> validation = validate_model(model)
>>> if validation['overall_status'] == 'PASS':
... print("Model validation passed")
>>> else:
... print("Validation issues:", validation['issues'])
"""
if checks is None:
checks = [
'general_data',
'environment',
'objects',
'connections',
'statics'
]
issues = []
warnings = []
# 1. General Data Check
if 'general_data' in checks:
general = model.general
# Check time steps
if general.InnerTimeStep > general.TargetLogSampleInterval:
issues.append(
f"InnerTimeStep ({general.InnerTimeStep}) > "
f"TargetLogSampleInterval ({general.TargetLogSampleInterval})"
)
# Check simulation duration
if general.StageCount == 0:
issues.append("No simulation stages defined")
# Check thread count
if general.ThreadCount > 16:
warnings.append(f"High thread count: {general.ThreadCount}")
# 2. Environment Check
if 'environment' in checks:
env = model.environment
# Check water depth
if env.WaterDepth <= 0:
issues.append(f"Invalid water depth: {env.WaterDepth}")
# Check wave parameters
if env.WaveType == 'JONSWAP':
if env.WaveHs <= 0:
issues.append(f"Invalid wave Hs: {env.WaveHs}")
if env.WaveTp <= 0:
issues.append(f"Invalid wave Tp: {env.WaveTp}")
# 3. Objects Check
if 'objects' in checks:
# Check for duplicate names
object_names = [obj.name for obj in model.objects]
duplicates = [name for name in object_names if object_names.count(name) > 1]
if duplicates:
issues.append(f"Duplicate object names: {set(duplicates)}")
# Check line types
for obj in model.objects:
if obj.type == OrcFxAPI.otLine:
if obj.NumberOfSections == 0:
issues.append(f"Line {obj.name} has no sections")
# Check line lengths
for i in range(obj.NumberOfSections):
if obj.Length[i] <= 0:
issues.append(
f"Line {obj.name} section {i+1} has invalid length: "
f"{obj.Length[i]}"
)
# 4. Connections Check
if 'connections' in checks:
for obj in model.objects:
if hasattr(obj, 'Connection'):
connection = obj.Connection
if connection not in ['Fixed', 'Free'] and connection != '':
# Check if connected object exists
if not model.objects.Exists(connection):
issues.append(
f"Object {obj.name} connected to non-existent "
f"object: {connection}"
)
# 5. Statics Check
if 'statics' in checks:
try:
model.CalculateStatics()
# Check for warnings in statics
# (OrcaFlex API doesn't provide direct access to warnings,
# would need to check specific conditions)
except Exception as e:
issues.append(f"Statics calculation failed: {str(e)}")
# Overall status
overall_status = 'PASS' if len(issues) == 0 else 'FAIL'
return {
'overall_status': overall_status,
'issues': issues,
'warnings': warnings,
'checks_performed': checks
}
def check_mooring_system_integrity(
model: OrcFxAPI.Model,
vessel_name: str,
design_criteria: dict
) -> dict:
"""
Check mooring system against design criteria.
Args:
model: OrcaFlex model (with results)
vessel_name: Vessel object name
design_criteria: Dictionary with design limits
Returns:
Dictionary with integrity check results
Example:
>>> design_criteria = {
... 'max_offset': 50, # m
... 'max_tension_uls': 8000, # kN
... 'max_tension_als': 10000, # kN
... 'min_line_clearance': 5 # m
... }
>>> integrity = check_mooring_system_integrity(
... model,
... 'FPSO',
... design_criteria
... )
"""
results = {
'overall_status': 'PASS',
'checks': {}
}
# 1. Vessel offset check
vessel = model[vessel_name]
time, x = extract_time_series(model, vessel_name, 'X')
time, y = extract_time_series(model, vessel_name, 'Y')
offset = np.sqrt(x**2 + y**2)
max_offset = np.max(offset)
results['checks']['vessel_offset'] = {
'max_offset': max_offset,
'limit': design_criteria['max_offset'],
'status': 'PASS' if max_offset <= design_criteria['max_offset'] else 'FAIL'
}
# 2. Mooring line tension checks
mooring_lines = [
obj for obj in model.objects
if obj.type == OrcFxAPI.otLine and 'Mooring' in obj.name
]
line_tension_results = {}
for line in mooring_lines:
time, tension = extract_time_series(
model,
line.name,
'Effective Tension',
OrcFxAPI.oeEndA
)
stats = calculate_statistics(time, tension)
# Check against ULS limit
status = 'PASS' if stats['max'] <= design_criteria['max_tension_uls'] else 'FAIL'
line_tension_results[line.name] = {
'max_tension': stats['max'],
'mean_tension': stats['mean'],
'limit': design_criteria['max_tension_uls'],
'status': status
}
if status == 'FAIL':
results['overall_status'] = 'FAIL'
results['checks']['line_tensions'] = line_tension_results
# 3. Line clearance check (if seafloor specified)
# (Simplified - would need seabed profile)
return results
6. Advanced Analysis Techniques
def extreme_response_analysis(
model_path: Path,
sea_states: List[dict],
response_variable: tuple,
method: str = 'most_probable_maximum'
) -> dict:
"""
Extreme response analysis using irregular wave simulations.
Args:
model_path: Path to base model
sea_states: List of sea state parameters
response_variable: Tuple of (object_name, variable_name, object_extra)
method: 'most_probable_maximum' or 'rayleigh_distribution'
Returns:
Extreme response statistics
Example:
>>> sea_states = [
... {'Hs': 10.0, 'Tp': 14.0, 'duration': 3600, 'seeds': range(1, 11)},
... {'Hs': 12.5, 'Tp': 15.0, 'duration': 3600, 'seeds': range(1, 11)}
... ]
>>> extreme = extreme_response_analysis(
... model_path=Path('mooring.dat'),
... sea_states=sea_states,
... response_variable=('Mooring_1', 'Effective Tension', OrcFxAPI.oeEndA)
... )
>>> print(f"Most Probable Maximum: {extreme['mpm']:.1f} kN")
"""
all_maxima = []
for sea_state in sea_states:
print(f"Sea State: Hs={sea_state['Hs']}, Tp={sea_state['Tp']}")
for seed in sea_state['seeds']:
# Load model
model = OrcFxAPI.Model(str(model_path))
# Set sea state
model.environment.WaveHs = sea_state['Hs']
model.environment.WaveTp = sea_state['Tp']
model.environment.WaveSeed = seed
# Set duration
model.general.StageDuration[0] = 100 # Build-up
model.general.StageDuration[1] = sea_state['duration']
# Run simulation
model.RunSimulation()
# Extract response
obj_name, var_name, obj_extra = response_variable
time, response = extract_time_series(model, obj_name, var_name, obj_extra)
# Get maximum (excluding build-up)
mask = time >= 100
max_response = np.max(response[mask])
all_maxima.append({
'Hs': sea_state['Hs'],
'Tp': sea_state['Tp'],
'seed': seed,
'max': max_response
})
print(f" Seed {seed}: Max = {max_response:.2f}")
# Calculate statistics
maxima_values = [m['max'] for m in all_maxima]
# Most Probable Maximum (mean of maxima)
mpm = np.mean(maxima_values)
# Rayleigh distribution fit
# For Rayleigh: mean_max = sigma * sqrt(2*ln(N))
# where N = number of cycles
# Solve for sigma
N_cycles_estimate = sea_states[0]['duration'] / sea_states[0]['Tp']
sigma_rayleigh = mpm / np.sqrt(2 * np.log(N_cycles_estimate))
# Extreme value (e.g., 10000-year return period)
# Using Rayleigh: X_extreme = sigma * sqrt(2*ln(N_extreme))
N_extreme = 10000 * 365 * 24 * 3600 / sea_states[0]['Tp'] # 10000 years
extreme_rayleigh = sigma_rayleigh * np.sqrt(2 * np.log(N_extreme))
return {
'all_maxima': all_maxima,
'mpm': mpm,
'std_maxima': np.std(maxima_values),
'max_of_maxima': np.max(maxima_values),
'min_of_maxima': np.min(maxima_values),
'rayleigh_sigma': sigma_rayleigh,
'extreme_10000yr_rayleigh': extreme_rayleigh
}
Complete Examples
Example 1: Automated Mooring Analysis Workflow
from pathlib import Path
import OrcFxAPI
import numpy as np
import pandas as pd
def complete_mooring_analysis_workflow(
vessel_params: dict,
mooring_config: dict,
environment: dict,
design_criteria: dict,
output_dir: Path
) -> dict:
"""
Complete automated mooring analysis workflow.
Steps:
1. Create model
2. Run static analysis
3. Run dynamic analysis
4. Extract results
5. Check against criteria
6. Generate report
Example:
>>> vessel_params = {
... 'name': 'FPSO',
... 'mass': 150000,
... 'length': 300,
... 'draft': 20
... }
>>> mooring_config = {
... 'pattern': 'spread',
... 'line_count': 8,
... 'line_length': 1500,
... 'fairlead_radius': 40,
... 'anchor_radius': 1400,
... 'line_sections': [
... {'type': 'R4 Studless Chain', 'length': 300},
... {'type': '76mm Wire', 'length': 900},
... {'type': 'R4 Studless Chain', 'length': 300}
... ]
... }
>>> environment = {
... 'water_depth': 1200,
... 'current_speed': 1.0,
... 'wave_height': 8.0,
... 'wave_period': 13.0
... }
>>> design_criteria = {
... 'max_offset': 50,
... 'max_tension_uls': 8000,
... 'safety_factor': 2.5
... }
>>> results = complete_mooring_analysis_workflow(
... vessel_params,
... mooring_config,
... environment,
... design_criteria,
... Path('mooring_analysis_results')
... )
"""
output_dir.mkdir(parents=True, exist_ok=True)
print("="*70)
print("AUTOMATED MOORING ANALYSIS WORKFLOW")
print("="*70)
# Step 1: Create model
print("\n[Step 1/6] Creating model...")
model = create_vessel_model(vessel_params, mooring_config, environment)
# Save data file
dat_file = output_dir / 'mooring_model.dat'
model.SaveData(str(dat_file))
print(f"Model saved: {dat_file}")
# Step 2: Validate model
print("\n[Step 2/6] Validating model...")
validation = validate_model(model)
if validation['overall_status'] == 'FAIL':
print("Model validation failed!")
print(f"Issues: {validation['issues']}")
return {'status': 'FAILED', 'reason': 'Model validation failed'}
print("Model validation passed")
# Step 3: Run static analysis
print("\n[Step 3/6] Running static analysis...")
model.CalculateStatics()
print("Static analysis complete")
# Extract static results
vessel = model[vessel_params['name']]
static_x = vessel.StaticResult('X')
static_y = vessel.StaticResult('Y')
static_offset = np.sqrt(static_x**2 + static_y**2)
print(f"Static offset: {static_offset:.2f} m")
# Step 4: Run dynamic simulation
print("\n[Step 4/6] Running dynamic simulation...")
model.RunSimulation()
# Save simulation
sim_file = output_dir / 'mooring_model.sim'
model.SaveSimulation(str(sim_file))
print(f"Simulation saved: {sim_file}")
# Step 5: Extract results
print("\n[Step 5/6] Extracting results...")
# Vessel motions
time, x = extract_time_series(model, vessel_params['name'], 'X')
time, y = extract_time_series(model, vessel_params['name'], 'Y')
offset = np.sqrt(x**2 + y**2)
offset_stats = calculate_statistics(time, offset)
print(f"Vessel offset - Max: {offset_stats['max']:.2f} m, "
f"Mean: {offset_stats['mean']:.2f} m")
# Mooring line tensions
mooring_results = {}
for i in range(mooring_config['line_count']):
line_name = f"Mooring_{i+1}"
time, tension = extract_time_series(
model,
line_name,
'Effective Tension',
OrcFxAPI.oeEndA
)
stats = calculate_statistics(time, tension)
mooring_results[line_name] = stats
print(f"{line_name} - Max: {stats['max']:.1f} kN, Mean: {stats['mean']:.1f} kN")
# Step 6: Check design criteria
print("\n[Step 6/6] Checking design criteria...")
integrity = check_mooring_system_integrity(
model,
vessel_params['name'],
design_criteria
)
print(f"Design check status: {integrity['overall_status']}")
# Generate summary report
summary = {
'status': 'SUCCESS',
'files': {
'model': dat_file,
'results': sim_file
},
'static_results': {
'offset': static_offset
},
'dynamic_results': {
'offset': offset_stats,
'mooring_tensions': mooring_results
},
'design_check': integrity
}
# Save summary to JSON
import json
summary_file = output_dir / 'analysis_summary.json'
with open(summary_file, 'w') as f:
# Convert numpy types to native Python types for JSON serialization
json.dump(summary, f, indent=2, default=lambda x: float(x) if isinstance(x, np.number) else str(x))
print(f"\nSummary saved: {summary_file}")
print("\n" + "="*70)
print("WORKFLOW COMPLETE")
print("="*70)
return summary
# Run workflow
vessel_params = {
'name': 'FPSO',
'mass': 150000,
'length': 300,
'draft': 20,
'draft_fore': 20,
'draft_aft': 20
}
mooring_config = {
'pattern': 'spread',
'line_count': 8,
'line_length': 1500,
'fairlead_radius': 40,
'anchor_radius': 1400,
'line_sections': [
{'type': 'R4 Studless Chain', 'length': 300},
{'type': '76mm Wire', 'length': 900},
{'type': 'R4 Studless Chain', 'length': 300}
]
}
environment = {
'water_depth': 1200,
'current_speed': 1.0,
'wave_height': 8.0,
'wave_period': 13.0
}
design_criteria = {
'max_offset': 50,
'max_tension_uls': 8000,
'safety_factor': 2.5
}
workflow_results = complete_mooring_analysis_workflow(
vessel_params,
mooring_config,
environment,
design_criteria,
Path('mooring_analysis_results')
)
Best Practices
1. Model Organization
# Naming conventions
NAMING_CONVENTIONS = {
'vessels': 'VesselName', # e.g., 'FPSO', 'FSO_1'
'lines': 'Mooring_N' or 'Riser_N', # e.g., 'Mooring_1', 'Riser_2'
'buoys': 'Buoy_N' or 'Subsurface_Buoy_N',
'6d_buoys': '6DBuoy_N',
'winches': 'Winch_N',
'line_types': 'Descriptive_Name', # e.g., 'R4_Studless_Chain', '76mm_Wire'
}
# Model structure best practices
MODEL_STRUCTURE = {
'stages': [
'Build-up', # 100-200s
'Main simulation', # 3600-10800s
'Optional: Transient event'
],
'time_steps': {
'inner': 0.01, # 0.01-0.05s
'log_sample': 0.1 # 0.1-1.0s
}
}
2. Simulation Settings
# Recommended simulation settings
SIMULATION_SETTINGS = {
'implicit': {
'use_variable_timestep': 'Yes',
'target_log_sample_interval': 0.1,
'inner_timestep': 0.01,
'max_iterations': 20,
'tolerance': 1e-6
},
'explicit': {
'timestep': 0.001, # Much smaller for explicit
'log_sample_interval': 0.1
}
}
3. Error Handling
def safe_simulation_run(
model: OrcFxAPI.Model,
max_retries: int = 3
) -> bool:
"""
Run simulation with error handling and retries.
Args:
model: OrcaFlex model
max_retries: Maximum number of retry attempts
Returns:
True if successful, False otherwise
"""
for attempt in range(max_retries):
try:
model.RunSimulation()
return True
except OrcFxAPI.DynamicsError as e:
print(f"Dynamics error (attempt {attempt+1}): {e}")
# Try reducing time step
current_dt = model.general.InnerTimeStep
model.general.InnerTimeStep = current_dt * 0.5
print(f"Reducing time step to {model.general.InnerTimeStep}")
except Exception as e:
print(f"Unexpected error: {e}")
return False
return False
Resources
OrcaFlex Documentation
- OrcaFlex Help: Built-in help system (F1 in OrcaFlex)
- Python API Reference: OrcaFlex installation → Python folder → OrcFxAPIDocumentation.html
- Example Scripts: OrcaFlex → Examples → Python folder
- Orcina Website: https://www.orcina.com/resources/
Training and Support
- Orcina Training Courses: Official OrcaFlex training
- User Forum: https://www.orcina.com/forums/
- Technical Support: support@orcina.com
Related Standards
- DNV-RP-C205: Environmental Conditions and Environmental Loads
- DNV-RP-F205: Global Performance Analysis of Deepwater Floating Structures
- API RP 2SM: Recommended Practice for Design, Manufacture, Installation, and Maintenance of Synthetic Fiber Ropes
- API RP 2SK: Design and Analysis of Stationkeeping Systems for Floating Structures
Additional Resources
- Noble Denton (2013). OrcaFlex Training Manual
- Orcina (2023). OrcaFlex Manual Version 11.4
- Various industry webinars and tutorials on YouTube
Use this skill for: Expert-level OrcaFlex modeling, automation, and analysis for offshore marine simulations with full Python API integration.
Skills Info
Original Name:orcaflex-specialistAuthor:vamseeachanta
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