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 *	str	Result name	Name of the result dataset.
num_samples *	int	Samples	Number of samples for Bernoulli distribution.

Input Datasets

key name	type	name	description
pipeline_repair_rate_damage *	ergo:pipelineDamageVer3	pipeline Damage	Output of the pipeline damage repair rate analysis

Output Datasets

key name	type	parent key	name	description
failure_probability *	incore:failureProbability	Results	A dataset containing failure probability results (format: CSV).
sample_failure_state *	incore:sampleFailureState	Results	A dataset containing failure state for each sample (format: CSV).

(* 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