Fatigue-constrained jacket optimization using Heuristic Particle Elimination Optimization algorithm with catalogue-discrete variables and conical joint modelling

This article presents a practical framework for minimizing the mass of offshore wind jacket structures under fatigue constraints with fully discrete design variables. The Heuristic Particle Elimination Optimization (HPEO) algorithm is employed to navigate a predefined catalogue of tubular diameters and wall thicknesses, ensuring manufacturable solutions without continuous relaxation or rounding. The framework integrates detailed fatigue verification with catalogue-based discrete sizing and explicit modelling of conical transition joints. The effectiveness of this approach is demonstrated on a jacket support structure for an offshore wind turbine under requirements related to natural frequencies, ultimate strength, and fatigue life. A coupled aero–hydro–servo–elastic model is used to compute the dynamic response under wind and wave loading. Stress concentration factors (SCFs) are obtained using Efthymiou parametric expressions, and hot-spot stresses at eight chord and brace locations are evaluated through superposition of axial, in-plane, and out-of-plane components; additional hot-spot stresses at conical transitions are also included. Fatigue damage is computed using rainflow counting and Miner’s rule. Results show that conical transitions can govern fatigue, highlighting the need to model them explicitly. Refining the design discretization from a coarse model (six pipe families) to a fine model (32 members with explicit cans and stubs) reduced the optimized jacket mass by up to 26.5%, with all natural-frequency, strength, and fatigue constraints still satisfied. Despite large initial particle pools, fewer than 5% in the coarse and 1 0 − 3 % in the fine discretization required full analyses, demonstrating computational savings of several orders of magnitude compared with population-based heuristics. By discarding non-promising candidates early, the HPEO framework converges to optimal or near-optimal designs that satisfy both mass reduction and fatigue life requirements. • Fatigue-constrained mass minimization with catalogue-discrete sizes. • Coupled time-domain aero–hydro–servo–elastic response to wind–wave loading. • Elimination-guided random search focuses full analyses on promising designs. • Fatigue evaluation via rainflow counting and Miner’s rule. • Conical transitions (cans/stubs) identified as governing fatigue hotspots.