Pipeline functionality analysis
Pipeline functionality analysis#
Description
This analysis computes pipeline functionality using repair rate calculations from pipeline damage analysis. The computation operates by computing Monte Carlo samples derived from Poisson sample deviates from the damage analysis as input to Bernoulli experiments, later used to determine average functionality.
The output of this analysis are two CSV files; a failure proability base_name_failure_probability.csv and base_name_failure_state.csv.
Input Parameters
key name |
type |
name |
description |
---|---|---|---|
|
|
Result name |
Name of the result dataset. |
|
|
Samples |
Number of samples for Bernoulli distribution. |
Input Datasets
key name |
type |
name |
description |
---|---|---|---|
|
|
pipeline Damage |
Output of the pipeline damage repair rate analysis |
Output Datasets
key name |
type |
parent key |
name |
description |
---|---|---|---|---|
|
|
Results |
A dataset containing failure probability results |
|
|
|
Results |
A dataset containing failure state for each sample |
(* required)
Execution
code snippet:
# Create pipeline functionality
pipline_func = PipelineFunctionality(client)
# Load input datasets
pipline_func.load_remote_input_dataset("pipeline_repair_rate_damage", pipeline_damage_id)
# Load fragility mapping
# Set analysis parameters
pipline_func.set_parameter("result_name", result_name)
pipline_func.set_parameter("num_samples", 100)
# Run pipeline analysis
result = pipline_func.run_analysis()
full analysis: pipeline_functionality.ipynb