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<p>This work presents TenMARO, a new Tent Mapping Artificial Rabbit Optimization algorithm, designed for effective fault parameter optimization in Gas Turbine Aero Engines (GT-AE). As opposed to the conventional metaheuristics, TenMARO combines deterministic tent mapping with adaptive foraging strategies to avoid convergence stagnation and local optimum difficulties. Fault parameters like combustor zone fractions and geometric dimensions are minimized with this algorithm after fault identification by utilizing insights from digital twin simulations and sensor streams. Tested on the NASA C-MAPSS dataset and data generated from the digital twin, Comparative experiments against PSO, GWO, ARO, and improved variants (IPSO, CGWO) show TenMARO achieving a 97.84 fitness score and 39% faster runtime. The framework demonstrates feasibility for real-time adaptive maintenance and predictive aero-engine control.</p>

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TenMARO-Driven Optimization in Multimodal Aero Engine Digital Twins

  • 期刊论文

  • 发表日期：

    2026年6月23日

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  • Naga Venkata Rama Subbarao Tadepalli,
  • Ruchita Valli Mallapragada,
  • Ramji Koona

摘要

This work presents TenMARO, a new Tent Mapping Artificial Rabbit Optimization algorithm, designed for effective fault parameter optimization in Gas Turbine Aero Engines (GT-AE). As opposed to the conventional metaheuristics, TenMARO combines deterministic tent mapping with adaptive foraging strategies to avoid convergence stagnation and local optimum difficulties. Fault parameters like combustor zone fractions and geometric dimensions are minimized with this algorithm after fault identification by utilizing insights from digital twin simulations and sensor streams. Tested on the NASA C-MAPSS dataset and data generated from the digital twin, Comparative experiments against PSO, GWO, ARO, and improved variants (IPSO, CGWO) show TenMARO achieving a 97.84 fitness score and 39% faster runtime. The framework demonstrates feasibility for real-time adaptive maintenance and predictive aero-engine control.