Offshore Jacket Structural Analysis using PyAnsys

Overview

Context and intent

This project explores a repeatable structural-analysis workflow for an offshore jacket using Python-assisted engineering tools. The emphasis is on traceability: every modelling choice, solver step, validation check, and reporting output should be understandable and reproducible.

Problem statement

Offshore structural analysis becomes difficult to review when geometry, loads, solver operations, and reporting are disconnected. The study asks how a Python-assisted workflow can make those steps more consistent without replacing engineering judgment.

Objectives

• Create a clear, parameter-driven model-building sequence.
• Separate modelling assumptions from solver execution logic.
• Capture checks that make results easier to challenge and reproduce.
• Produce reporting outputs that state scope and limitations explicitly.

Engineering approach

A reviewable way of working

• Define geometry, material, and section inputs as readable parameters.
• Build analysis stages as small, auditable operations.
• Record solver messages and extract only the results needed for review.
• Compare automated outputs with independent checks before interpretation.

Assumptions

• The current model is a learning model rather than a project-specific design model.
• Loads, boundary conditions, and sections remain subject to documented verification.
• No result is presented as suitable for certification or construction decisions.

Validation

Evidence before interpretation

• Review units, connectivity, restraints, and load directions before solving.
• Check solver completion and warnings rather than relying on a completion flag alone.
• Compare reactions, deformation shape, and selected hand checks for physical consistency.
• Retain an evidence trail that distinguishes solver success from engineering acceptance.

Results status

• Validated numerical results will be added only after the study reaches a documented review milestone.
• The current portfolio focus is the workflow architecture and validation method, not a design utilisation claim.

Key findings

What the workflow is teaching

• Automation is most useful when assumptions and checks remain visible.
• A repeatable report structure makes gaps easier to identify before results are shared.
• Solver output needs engineering interpretation; automation does not make it self-validating.

Lessons learned

• Design the validation plan before writing the automation layer.
• Keep input data, execution logic, evidence, and reporting clearly separated.
• Treat warnings and model-quality checks as first-class outputs.

Limitations

• This project is intended for learning, workflow development, and portfolio demonstration. It is not a certified design deliverable.
• The model does not represent a certified site, metocean basis, or design code assessment.
• Fatigue, accidental, fabrication, and installation checks are outside the current scope.

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