International Journal of Analytical, Experimental and Finite Element Analysis
Volume 13 · Issue 2 · June 2026 · pp. 88–100
Research Article · Peer Reviewed
Received: April 12, 2026 · Accepted: June 17, 2026 · Published: June 27, 2026
DOI: 10.26706/ijaefea.13.2.20260405
Open Access · CC BY 4.0

Dynamic Analysis and ANN based Optimization of Thermally Stressed Rotating High-Pressure Compressor Blades

Amarjeet Banjare1, Uttam Kumar Kar2,*

1Research Scholar, Mechanical Engineering Department, CCET, Bhilai-490026, Chhattisgarh, India
2Assistant Professor, Mechanical Engineering Department, CCET, Bhilai-490026, Chhattisgarh, India

Email: amarjeetbanjaresatna@gmail.com, ukkar093@gmail.com

*Correspondence: ukkar093@gmail.com

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Abstract

High-pressure compressor blades in aero-engines operate under severe thermo-mechanical environments characterized by high rotational speeds, elevated temperatures, and dynamic loading. Accurate prediction and optimization of their vibration behavior are essential for ensuring structural reliability and preventing resonance-induced failures. In this study, a high-pressure compressor blade is modeled as a thermally stressed rotating tapered beam, and its dynamic behavior is investigated using finite element analysis. The effects of rotational speed, temperature rise, material gradation, and aspect ratio on natural frequency and dynamic deflection are examined. A feed-forward Artificial Neural Network (ANN) is developed to predict vibration responses using finite element-generated datasets. The trained ANN model is subsequently integrated with Particle Swarm Optimization (PSO) and Multi-Objective Particle Swarm Optimization (MOPSO) algorithms to maximize natural frequency and minimize dynamic deflection simultaneously. Results indicate that rotational speed increases structural stiffness through centrifugal stiffening, while temperature rise reduces stiffness and vibration resistance. The ANN model predicts vibration characteristics with less than 5% error compared to finite element results. Optimization results demonstrate a significant improvement in natural frequency and vibration suppression, confirming the effectiveness of the proposed ANN-assisted optimization framework for rotating compressor blade design.

Keywords

Compressor Blade Rotating Beam Free Vibration Artificial Neural Network Optimization Thermo-mechanical Loading

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