dubi̇ted Duzce University Journal of Science and Technology 2148-2446 Duzce University 10.29130/dubited.1766366 Optimization Techniques in Mechanical Engineering Mechanical Engineering (Other) Makine Mühendisliğinde Optimizasyon Teknikleri Makine Mühendisliği (Diğer) Hidrojenle Zenginleştirilmiş Ön Karışımlı Yanmada Kararsızlık Rejimlerinin Fiziksel Olarak Yorumlanabilir Yapay Zekâ Destekli Tahmini Physically Interpretable AI-Driven Prediction of Instability Regimes in Hydrogen-Enriched Premixed Combustion https://orcid.org/0000-0001-9832-9880 Yılmaz Ali Can ÇUKUROVA ÜNİVERSİTESİ 04 19 2026 14 2 577 593 08 16 2025 03 09 2026 Copyright © 2013, Duzce University Journal of Science and Technology 2013 Duzce University Journal of Science and Technology

Hidrojenle zenginleştirilmiş yanma, yüksek verimli enerji sistemlerinin karbonsuzlaştırılmasının merkezinde yer alır; ancak ön karışımlı alevlerde ortaya çıkan termoakustik ve hidrodinamik kararsızlıklar, pratik uygulamadaki yaygınlaşmasını sınırlamaktadır. Bu çalışmada, yanma kararsızlığının öngörüsü ve fiziksel tanılanması için yüksek doğruluklu hesaplamalı akışkanlar dinamiği (CFD) simülasyonlarını yorumlanabilir derin öğrenme ile birleştiren yeni ve entegre bir çerçeve önerilmiştir. Eksensimetrik 1500 CFD simülasyonundan oluşan parametrik bir dizi yürütülmüş; hidrojen karışım oranları (hacimce %0–100), eşdeğerlik oranları (ϕ = 0.6–1.4) ve türbülans şiddetleri (%5–%25) sistematik olarak değiştirilmiştir. Kök-ortalama-kare (RMS) basınç, alev önü buruşması ve radikal havuzu dinamiği dâhil temel kararsızlık göstergeleri, hem kararlı hem de kararsız alev rejimlerinden çıkarılmıştır. Toplanan veriler, hibrit bir evrişimsel sinir ağı–uzun-kısa süreli bellek (CNN–LSTM) modelini eğitmek için kullanılmış; model, ikili rejim sınıflandırmasında %94.3 test doğruluğu, %94.4 F1-skoru ve 0.978 AUC-ROC değeri elde etmiştir. SHAP tabanlı yorumlanabilirlik analizi, model tahminlerinin fiziksel olarak anlamlı özniteliklere dayandığını; RMS basınç, OH dalgalanmaları ve baskın akustik frekansların başlıca katkı sağlayanlar olduğunu göstermiştir. Yapay zekâ ile öngörülen kararsızlık rejim haritaları, CFD’den türetilen kararsızlık eşiklerinin %88.6’sı ile çakışarak yaklaşımın fiziksel tutarlılığını ortaya koymuştur. Alan görselleştirmeleri, kararsız rejimlerin (ϕ = 1.1, H₂ = %80) kararlı alevlere kıyasla belirgin alev önü buruşması, daha geniş yüksek sıcaklık bölgeleri ve uzamsal olarak dağıtılmış radikal üretimi sergilediğini göstermiştir. Bu yaklaşım, veri odaklı ve fiziksel olarak yorumlanabilir kararsızlık tanılaması için umut verici bir yol açmakta; brülör tasarımı, işletme güvenliği ve hidrojen temelli sistemlerde gerçek zamanlı yanma izlemesine doğrudan etki potansiyeli taşımaktadır. Gelecek çalışmalarda, yöntemin çoklu yakıt konfigürasyonlarına ve gerçek dünya uygulamaları için deneysel entegrasyona genişletilmesi hedeflenmektedir.

Hydrogen-enriched combustion is central to decarbonizing high-efficiency energy systems, yet its practical adoption is limited by the onset of thermoacoustic and hydrodynamic instabilities in premixed flames. In this study; a novel, integrated framework that combines high-fidelity computational fluid dynamics (CFD) simulations with interpretable deep learning for the prediction and physical diagnosis of combustion instability was proposed. A parametric suite of 1,500 axisymmetric CFD simulations was carried out, systematically varying hydrogen blending ratios (0–100% by volume), equivalence ratios (ϕ = 0.6–1.4), and turbulence intensities (5–25%). Key instability markers including root-mean-square (RMS) pressure, flame front wrinkling, and radical pool dynamics were extracted from both stable and unstable flame regimes. The data collected was used to train a hybrid convolutional neural network–long short-term memory (CNN–LSTM) model, which achieved a test accuracy of 94.3%, F1-score of 94.4%, and area under the receiver operating characteristic curve (AUC-ROC) of 0.978 in binary regime classification. SHAP-based interpretability analysis demonstrated that the model’s predictions were grounded in physically relevant features, with RMS pressure, OH fluctuations, and dominant acoustic frequencies serving as the principal contributors. AI-predicted instability regime maps showed an 88.6% overlap with CFD-derived instability thresholds, highlighting the physical consistency of the approach. Distinct field visualizations showed that unstable regimes (ϕ = 1.1, H₂ = 80%) exhibit pronounced front wrinkling, broader high-temperature zones, and spatially distributed radical production compared to stable flames. This approach opens a promising path for data-driven, physically interpretable instability diagnostics, which could directly impact for burner design, operational safety, and real-time combustion monitoring in hydrogen-based systems. In future work, it is aimed to extend this approach to multi-fuel configurations and experimental integration for real-world deployment.

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