Celtic Sea Floating Offshore Wind – Layout Generation & Topology Optimization

Executive Summary

This study establishes the layout generation layer of Morie Analytics by transforming site-level geospatial data into engineering-ready floating wind farm configurations.

Using bathymetry and seabed classification from morie_site, the workflow identifies feasible regions, generates a constrained hexagonal lattice, and selects optimal floater clusters using topology-driven optimization.

The result is a deterministic and reproducible layout generation framework that replaces heuristic placement strategies with structured engineering logic.

This module represents the stage where site constraints are translated into spatial system configuration.

Site intelligence → Layout generation → Soil reconstruction → Mooring physics → Anchor verification → Cable optimization

Project Scope

• Site-driven layout generation based on processed geospatial data
• Bathymetry and soil-constrained feasibility filtering
• Hexagonal lattice-based floater placement
• Topology-driven cluster optimization
• YAML-based model generation for downstream workflows
• Integration with mooring and anchor modules

This study converts site intelligence into structured spatial design decisions.

Engineering Context

Following site characterization, the next critical step in offshore wind design is:

Where do we place the turbines?

At this stage, engineers must balance:

• Lease boundaries and setbacks
• Water depth constraints
• Seabed conditions
• Mooring footprint interactions
• Anchor-sharing potential

Traditional approaches rely on manual placement or coarse grids.

This workflow introduces a lattice-driven methodology, where layout is derived from physical feasibility, spatial structure, and system connectivity.

Inputs and Data Sources

This study builds directly on upstream Morie Analytics outputs:

From morie_site

• Bathymetry grids (bathy_*.txt)
• Soil classification grids (soil_*.txt)
• Lease boundary definitions

Additional Inputs

• Layered soil profiles (YAML mapping)
• Layout parameters (spacing, buffer, orientation)

All inputs are aligned in a common projected coordinate system.

This provides the spatial constraints required for layout generation.

Technical Architecture

The workflow is implemented in Python using:

• numpy, scipy → numerical operations
• matplotlib → visualization
• pyyaml → configuration management
• famodel → system definition and integration
• MoorPy → mooring system compatibility

Core modules:

• load_moorpy_grid → bathymetry and soil grid loading
• suitability_filtering → depth (88–94 m) and soil-based feasibility masking
• generate_hex_turbine_array → constrained lattice generation
• slide_fixed8_best → cluster optimization
• inject_selected_cluster_into_yaml → layout integration into system model
• merge_coincident_anchors → shared-anchor topology definition

System Flow

Site Constraints → Lattice Generation → Cluster Optimization → System Configuration

The architecture ensures direct continuity with downstream physics-based modules.

Processing Workflow

1. Load bathymetry and soil data from morie_site
2. Build engineering suitability mask
3. Generate hexagonal lattice within buffered lease
4. Filter valid floater positions
5. Slide cluster template across lattice
6. Evaluate connectivity metrics
7. Select optimal cluster
8. Inject layout into project YAML
9. Instantiate mooring systems and anchors
10. Merge shared anchors and extract topology

This converts site constraints into structured floating wind farm layouts.

Bathymetry & Soil Context

Figure 1 – Lease-scale bathymetry used to constrain feasible layout regions.

Figure 2 – Seabed classification based on EMODnet Folk-7 system.

Engineering Significance

These datasets define:

• Feasible depth ranges
• Soil conditions compatible with anchor systems
• Spatial constraints for layout generation

Suitability Region Detection

Figure 3 – Feasible region derived from combined bathymetry and soil constraints.

Criteria

• Depth: from 88 m to 94 m
• Soil: Engineering-suitable sediments - 2.0. Sand (Multiscale - folk 7)

Engineering Significance

Defines the valid design domain where:

• Standardization of the assets
• Mooring configurations are coherent across the site
• Anchor systems are viable
• Installation equipment and techniques are feasible

Hexagonal Lattice Generation

Figure 4 – Hexagonal lattice filtered by feasibility constraints.

Parameters

• Spacing: 800 m
• Buffer: 400 m
• Orientation: 30°

Engineering Significance

The hex grid ensures:

• Uniform spacing
• Deterministic placement
• Compatibility with shared-anchor layouts

Cluster Optimization (Topology-Driven)

Figure 5 – Optimal 8-node cluster selected based on connectivity metrics.

Methodology

• Slide fixed cluster across lattice
• Evaluate feasibility
• Compute connectivity metrics

Key Metrics

• avg_neighbors → compactness
• min_neighbors → weakest node
• score → overall topology quality

Engineering Insight

Connectivity acts as a proxy for shared-anchor efficiency, enabling layout optimization without full physics-based modeling.

Mooring & Anchor Topology Generation

Figure 6 – Anchor-sharing topology derived from mooring system generation.

Outputs

• Anchor coordinates
• Attachment counts (1–3 lines per anchor)
• Shared anchor configurations

Engineering Significance

This step defines:

• Load aggregation structure
• Anchor-sharing potential
• Inputs for anchor sizing

Outputs Generated

• Optimized floater coordinates
• Modified project YAML (modified_celticsea.yaml)
• Suitability maps
• Hex grid and cluster visualizations
• Anchor topology maps

These outputs are directly usable in:

• Mooring analysis
• Anchor design
• Cable routing

Engineering Applications

The outputs support:

• Layout feasibility screening
• Shared-anchor optimization
• Mooring spacing definition
• Pre-anchor design workflows
• Farm-scale planning

This enables:

Manual positioning → Engineering-driven system design

Relationship to Other Morie Study Cases

Receives from:

• morie_site → bathymetry, soil, lease constraints

Feeds into:

• morie_soil → localized soil modeling around selected cluster
• morie_mooring → system geometry and equilibrium analysis
• morie_anchor → anchor load and capacity design
• morie_cable → cable routing and configuration

This module is the bridge between site data and system design.

Why It Matters Commercially

Layout decisions strongly influence downstream cost and feasibility.

• Reduces uncertainty in early-stage layout decisions
• Enables rapid comparison of layout scenarios
• Improves anchor-sharing efficiency
• Supports scalable farm design workflows
• Bridges GIS and engineering domains

This is where:

• Layout becomes a strategic design variable
• System configuration drives cost and performance
• Engineering replaces heuristic placement

Aspects to Improve

• Multi-cluster optimization across full lease
• Integration with dynamic load analysis (RAFT)
• Inclusion of cable constraints in optimization
• Cost-driven objective functions
• Machine learning-based layout selection

Design Philosophy

This study reflects the Morie Analytics approach:

• Physics-informed
• Modular
• Traceable
• Engineering-focused
• Scalable

How to Run

1. Place input datasets in celtic_sea_share/

2. Install dependencies:

   • numpy
   • matplotlib
   • scipy
   • pyyaml
   • FAModel
   • MoorPy
3. Execute:

python morie_layout.py