A Sequence-to-Sequence Transformer-Based Approach for Turbine Blade Profile Optimization

Artificial intelligence (AI) is playing an increasingly important role in industrial design, particularly in the aerodynamic optimization of turbine components in aero-engines. This study proposes a turbine blade profile optimization method based on a sequence-to-sequence (Seq2Seq) transformer model. By drawing an analogy between language translation and geometric design generation, the method adopts an encoder–decoder architecture to learn the mapping between blade geometry and its aerodynamic performance. To enhance the interpretability and reliability of model outputs, a performance-matching evaluation framework is introduced. Inspired by similarity metrics in natural language processing, this framework proposes quantifiable indicators to assess the deviation between the predicted aerodynamic performance and the design targets. In a turbine design optimization case, the proposed method successfully generates blade profiles that meet predefined aerodynamic performance requirements, with the optimized design showing a 10.9% reduction in total pressure loss coefficient (from 0.744 to 0.663) and a 0.53% increase in total pressure recovery coefficient (from 0.949 to 0.954), verifying the effectiveness of the Seq2Seq transformer model in capturing design capabilities. It also demonstrates the practical value of performance-matching metrics in evaluating deep learning-assisted design. Taken together, AI-driven optimization approaches hold great promise for aerodynamic design in the energy sector.