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المؤلفSadeq, Abdellatif M.
المؤلفMoghaddam, Amin Hedayati
المؤلفSleiti, Ahmad K.
المؤلفAhmed, Samer F.
تاريخ الإتاحة2024-03-25T08:11:17Z
تاريخ النشر2024-02-15
اسم المنشورKorean Journal of Chemical Engineering
المعرّفhttp://dx.doi.org/10.1007/s11814-024-00086-5
الاقتباسSadeq, A. M., Moghaddam, A. H., Sleiti, A. K., & Ahmed, S. F. (2024). Development of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends. Korean Journal of Chemical Engineering, 1-16.
الرقم المعياري الدولي للكتاب0256-1115
معرّف المصادر الموحدhttp://hdl.handle.net/10576/53437
الملخصStudying the spatial and temporal evolution in turbulent flames represents one of the most challenging problems in the combustion community. Based on previous 3D numerical analyses, this study aims to develop data-driven machine learning (ML) models for predicting the flame radius evolution and turbulent flame speeds for diesel, gas-to-liquids (GTL), and their 50/50 blend (by volumetric composition) under different thermodynamic and turbulence operating conditions. Two ML models were developed in this study. Model 1 predicts the variations of the flame radius with time, equivalence ratio, and turbulence intensity, whereas model 2 predicts the variations of the turbulence flame speed with the operating parameters. The k-fold cross-validation technique is used for model training, and the developed neural network-based model is used to investigate the effects of operating parameters on the premixed turbulent flames. In addition, the possible minimum and maximum values of responses at the corresponding operating parameters are found using a genetic algorithm (GA) approach. Model 1 could capture the computational fluid dynamics (CFD) outputs with high precision at different flame radiuses and time instants with a maximum absolute error percentage of 5.46%. For model 2, the maximum absolute error percentage was 6.58%. Overall, this study demonstrates the applicability and promising performance of the proposed ML models, which will be used in subsequent research to analyze turbulent flames a posteriori.
راعي المشروعOpen Access Funding Provided by the Qatar National Library.
اللغةen
الناشرSpringer Nature
الموضوعArtificial intelligence
Flame radius evolution
GTL
Machine learning model
Turbulent flame speed
Turbulent premixed flame
العنوانDevelopment of Machine Learning Models for Studying the Premixed Turbulent Combustion of Gas-To-Liquids (GTL) Fuel Blends
النوعArticle
الصفحات1-16
رقم العدد2
رقم المجلد41
ESSN1975-7220
dc.accessType Open Access


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