Celtic Sea Floating Offshore Wind – Layout Generation & Spatial Topology Screening

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 screening.

The result is a deterministic and reproducible layout generation framework that replaces heuristic placement with engineering-driven system configuration. In conventional workflows, layout is often treated as a geometric or wind-driven problem.

In floating wind, it is a system design problem. The reference case corresponds to a 120 MW floating wind cluster, bridging layout generation with real project-scale design.

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 screening
• Model generation for downstream workflows
• Integration with mooring and anchor modules

This study converts site intelligence into structured spatial system design decisions.

The reference configuration consists of:

• 8 floating wind turbine generators (WTGs)
• 15 MW nominal capacity per unit
• Total installed capacity: 120 MW

This defines a cluster-scale floating wind system, used to evaluate layout feasibility, topology optimization and downstream engineering workflows.

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
• Wake effects and overall wind turbine performance
• Seabed conditions
• Mooring footprint interactions
• Anchor-sharing potential

Traditional approaches rely on manual placement or coarse grids. These approaches fail to capture the strong coupling between layout, mooring systems and anchor design.

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

At this stage, wake interactions and AEP optimization are intentionally treated as secondary constraints.

The focus of this workflow is the generation of engineering-compatible spatial configurations suitable for downstream mooring, anchor and cable analysis.

Detailed aerodynamic optimization would require coupling with dedicated wake models and site-specific wind resource assessment.

Inputs and Data Sources

This study builds directly on upstream Morie Analytics outputs:

From morie_site

• Bathymetry and soil classification grids
• Lease boundary definitions

Additional Inputs

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

All inputs are aligned in a common projected coordinate system.

This provides the spatial constraints required for layout generation.

System Flow

Site Intelligence → Feasibility Filtering → Lattice Generation → Topology Screening → 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 and slope 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

Filtering relevant site criteria:

• Depth: from 88 m to 94 m
• Soil: Sediment classes preliminarily compatible with the selected anchor concept assumptions - 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 concepts remain preliminarily compatible with the selected site constraints
• Installation equipment and techniques are feasible

Hexagonal Lattice Generation

Figure 4 – Hexagonal lattice filtered by feasibility constraints.

The hexagonal structure provides uniform neighbor relationships and consistent spatial spacing, which are advantageous for evaluating shared-anchor compatibility and mooring connectivity patterns.

Parameters

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

The selected spacing is used here as a demonstrative engineering configuration and should not be interpreted as an aerodynamically optimized arrangement for commercial-scale deployment.

Engineering Significance

The hex grid ensures:

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

Cluster Optimization (Topology-Driven)

Figure 5 – Selected 8-node cluster identified through topological compactness screening.

The selected cluster represents a 120 MW floating wind configuration, enabling system-level evaluation at a realistic project scale.

Methodology

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

Key Metrics

• avg_neighbors → compactness
• min_neighbors → weakest node
• score → Topological Compactness Index (TCI)

Engineering Insight

The Topological Compactness Index (TCI) is introduced here as a spatial screening metric intended to identify geometric configurations potentially compatible with shared-anchor mooring arrangements.

The metric evaluates local floater connectivity and spatial compactness within the lease area prior to detailed mooring generation.

At this stage, the TCI should not be interpreted as:

• A wake optimization metric
• An annual energy production (AEP) indicator
• A hydrodynamic efficiency metric
• A full-system economic optimization metric

Instead, it acts as a first-order indicator of geometric proximity and potential anchor-sharing compatibility.

Actual shared-anchor feasibility remains dependent on downstream engineering parameters including:

• Mooring heading assignment
• Anchor radius
• Mooring line orientation
• Dynamic system response
• Boundary effects within the lease area

This enables early-stage spatial screening prior to detailed physics-based analysis.

Mooring & Anchor Topology Generation

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

This step effectively defines the load pathways of the system, linking spatial layout to structural behavior.

Outputs

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

Engineering Significance

This step defines:

• Load aggregation structure
• Anchor-sharing potential
• Location inputs for anchor sizing

System-Level Representation

Figure 7 – Integrated system view combining bathymetry, layout, mooring configuration, and anchor positions.

Engineering Significance

This visualization represents the transition from spatial layout to physical system configuration.

It captures:

• Bathymetry-driven placement of floaters
• Mooring system geometry and orientation
• Anchor locations and sharing structure
• Interaction between layout and subsea infrastructure

This is where layout becomes an engineered offshore system.

Outputs Generated

For selected lease area:

• Selected floater coordinates
• Modified project file
• 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 and lease area boundary constraints

Feeds into:

• morie_soil → localized soil modeling around selected cluster
• morie_mooring → system geometry and equilibrium analysis
• morie_anchor → anchor load assessment and capacity design check
• 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 CAPEX, installation strategy, and system feasibility.

• Reduces uncertainty in early-stage layout decisions
• Enables rapid comparison of layout scenarios
• Improves mooring- and 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 and wake effects for production assessment
• 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