Modeling the combustion emissions of sustainable aviation fuel blends

Creative Commons License

Kılıç D., YILDIZ M., Matijošius J., Kale U.

Journal of Thermal Analysis and Calorimetry, 2026 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Basım Tarihi: 2026
  • Doi Numarası: 10.1007/s10973-026-15620-2
  • Dergi Adı: Journal of Thermal Analysis and Calorimetry
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Chemical Abstracts Core, Chimica, Compendex, Index Islamicus, INSPEC, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO), Materials Science & Engineering Collection (ProQuest), Technology Collection (ProQuest)
  • Anahtar Kelimeler: Emissions, Gas turbine engine, Impact analyses, Sustainable aviation fuels
  • Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
  • Erciyes Üniversitesi Adresli: Evet

Özet

Assessing the thermodynamic and environmental performance of alternative fuel blends is crucial for energy management and climate change mitigation within the aviation industry. In this study, a data-driven impact assessment utilizing surrogate models was employed to evaluate the steady-state performance and emission characteristics of a turboprop gas turbine engine. Three synthetic paraffinic kerosene (HEFA, ATJ-SPK, and FT-SPK) blended with conventional Jet-A up to a 50% volumetric ratio were thoroughly investigated. Operating under a constant fuel mass flow rate boundary condition, the enhanced gravimetric energy density of the sustainable aviation fuels (SAFs) yielded a direct mechanical power augmentation, resulting in up to a 1.0% reduction in Specific Fuel Consumption (SFC). The most critical contribution of this research is the isolation and quantification of the “double-benefit” phenomenon through a power-normalized specific emission framework. By integrating the chemical reduction in carbon oxidation with thermodynamic efficiency gains, the results demonstrated that actual operational emissions are significantly lower than mass-balance predictions. At the 50% blend limit, soot formation plummeted by exactly 40%. Furthermore, power-normalized specific emissions demonstrated net operational reductions of 3.1% for CO2 and 3.06% for thermal NOx, which was effectively evaluated via a specialized quadratic surrogate model. Ultimately, this data-driven methodology emerges as a highly predictive and reliable tool for characterizing advanced fuel-engine integrations, facilitating data-driven decision-making to accelerate aviation’s transition to clean energy.