ESOX Python is our Python-based tool for high complexity sequential downtime modeling, which is run by our internal specialists on a consulting basis.
It has greater capabilities and more flexibility than our ESOX Excel tool, making it the perfect choice for especially challenging simulation scenarios.
As our ESOX Python service is consulting-based, we do not provide the tool itself to our clients.
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Automated generation of a task list for cycle-based work campaigns |
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Manual editing of individual tasks in the auto-generated task list |
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Multi-variable constraints |
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Hs/Tp curve workability limits for floating operations |
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Learning curve modeling |
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Generate weather calendars for P6 and MS Project |
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Milestone completion statistics |
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Root cause analysis |
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Annual campaign simulation for each reanalysis year |
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Global ERA5 metocean time series data |
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Time step log of simulation steps for validation of results |
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Daylight as a constraint |
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Yearly interruptions for any activity |
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Wind estimates at any height |
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Any number and type of time series variables |
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Parallel, interdependent activity streams |
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Shared inventory modeling |
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Shared resource modeling |
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Weather window requirements with non-constant limits |
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Advanced root cause analysis |
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Interruption for some but not all positions |
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Wind direction as a constraint |
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Separable activities with constraints during downtime |
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Activities with no downtime between them |
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Sailing delay |
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Multiple installation vessels with different start dates |
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Visualize the root cause for downtime, identify the bottlenecks and optimize your construction setup accordingly.
Dimension your storage capacity by modeling inbound and outbound logistics. Will the installation vessels run out of components in a P30 scenario? How likely is it that the storage capacity limit will be reached, stopping the inbound logistics?
(A) Risk of storage at max capacity (12 WGTs). Inbound logistics on standby until the next load-out of components.
(B) Risk of running out of WTGs at staging port. Coupling of load-out activities and storage level.
Model interdependent operational constraints, such as floating operations with interdependent wave periods and wave heights.
Fixed, individual limits on wave height and wave period leads to overly conservative downtime estimates. Identify all workable periods by modeling the correct Hs-Tp curves instead.
Contact our specialists with your simulation case or any questions about ESOX Python.
Project: Supporting an offshore wind project in the US Atlantic Coast by analyzing the transport and installation options.
Challenges: The logistic options including several vessels feeding components from different harbours to a main installation vessel located at the offshore site. Some of the questions asked were:
Outcome: ESOX Python helped to answer the questions by combining unparalleled modeling capabilities together with our specialists’ solid industry experience.