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