TY  - JOUR
T1  - Artificial Intelligence-Based Stress Prediction for Rotor Blisk in Gas Turbine Engines
TT  - Gaz Türbinli Motorlarda Rotor Bliskleri için Yapay Zeka Tabanlı Stres Tahmini
AU  - Kortağ, Ufuk
PY  - 2025
DA  - August
Y2  - 2025
DO  - 10.51785/jar.1674066
JF  - Journal of Aviation Research
JO  - JAR
PB  - İnan ERYILMAZ
WT  - DergiPark
SN  - 2687-3338
SP  - 149
EP  - 176
VL  - 7
IS  - 2
LA  - en
AB  - Gas turbine engines are critical components in aerospace, power generation, and industrial applications, consisting of complex rotating and stationary parts subject to extreme mechanical, thermal, and aerodynamic loads. A key component in modern gas turbines is the rotor blisk, which combines the blades and disk into a single unit. Due to its complex geometry and harsh operating conditions, the rotor blisk experiences significant mechanical stresses that must be accurately calculated to ensure reliability, safety, and optimal performance. Traditional methods, such as finite element analysis (FEA), are widely used to calculate stress distributions under various loading conditions. However, FEA is computationally expensive, especially when analyzing multiple scenarios for different operating conditions. This computational cost can become a bottleneck in iterative design studies and real-time decision making. To address this challenge, this study proposes a novel approach that uses deep learning to predict stresses in rotor blisks under varying loads. A deep neural network (DNN) was trained on FEA-generated stress data to learn the relationships between input parameters and resulting stress distributions. The AI-based model was validated using unseen load scenarios for radial, axial, and tangential stress distributions and maximum-minimum stress results, with a maximum deviation of 6% to 15% from FEA results. In addition, the Artificial Intelligence (AI) approach reduced the computational cost by 13,000 times faster than FEA by predicting results instead of solving complex equations. The AI approach enables rapid stress predictions and facilitates real-time design iteration and optimization. These results highlight the transformative potential of AI in engineering simulation, enabling faster, more efficient structural assessments and advancing the optimization of gas turbine components in the aerospace and energy industries.
KW  - Artificial Intelligence
KW  - Deep Learning
KW  - Gas Turbine Engine
KW  - Rotor Blisk
KW  - Finite Element Analysis
N2  - Gaz türbinli motorlar; havacılık, enerji üretimi ve endüstriyel uygulamalarda kritik bileşenler olup, aşırı mekanik, termal ve aerodinamik yüklere maruz kalan karmaşık döner ve sabit parçalardan oluşmaktadır. Modern gaz türbinlerinin temel bileşenlerinden biri, kanatları ve diski tek bir bütün halinde birleştiren rotor blisktir. Karmaşık geometrisi ve zorlu çalışma koşulları nedeniyle rotor blisk, güvenilirliğin, emniyetin ve optimal performansın sağlanabilmesi için doğru bir şekilde hesaplanması gereken önemli mekanik gerilmelere maruz kalmaktadır. Sonlu elemanlar analizi (SEA) gibi geleneksel yöntemler, farklı yükleme koşullarında gerilme dağılımlarını hesaplamak için yaygın şekilde kullanılmaktadır. Ancak, SEA özellikle farklı çalışma koşulları için çoklu senaryoların analizinde hesaplama açısından maliyetli olup, bu hesaplama yükü tekrarlamalı tasarım çalışmalarında ve gerçek zamanlı karar vermede bir darboğaz hâline gelebilmektedir. Bu zorluğun üstesinden gelmek amacıyla, bu çalışma rotor blisklerde farklı yükler altında gerilmeleri tahmin etmek için derin öğrenme kullanan yeni bir yaklaşım önermektedir. Bir derin sinir ağı (DSA), giriş parametreleri ile ortaya çıkan gerilme dağılımları arasındaki ilişkileri öğrenebilmek için SEA tarafından üretilmiş gerilme verileri üzerinde eğitilmiştir. Yapay zeka tabanlı model, radyal, eksenel ve teğetsel gerilme dağılımları ile maksimum-minimum gerilme sonuçları için görülmemiş yük senaryoları kullanılarak doğrulanmış ve SEA sonuçlarına kıyasla %6 ila %15 arasında maksimum sapma göstermiştir. Ayrıca, yapay zeka yaklaşımı karmaşık denklemleri çözmek yerine sonuçları tahmin ederek SEA’ya kıyasla hesaplama maliyetini 13.000 kat azaltmıştır. Yapay zeka yaklaşımı, hızlı gerilme tahminleri yapılmasını mümkün kılmakta ve gerçek zamanlı tasarım yinelemelerini ve optimizasyonu kolaylaştırmaktadır. Bu sonuçlar, mühendislik simülasyonunda yapay zekânın dönüştürücü potansiyelini vurgulamakta, daha hızlı ve daha verimli yapısal değerlendirmeleri mümkün kılmakta ve havacılık ile enerji endüstrilerinde gaz türbini bileşenlerinin optimizasyonunu ilerletmektedir.
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UR  - https://doi.org/10.51785/jar.1674066
L1  - https://dergipark.org.tr/en/download/article-file/4764259
ER  -