Physically Constrained Modeling of Gaseous Emissions from Aircraft Gas Turbine Engines Using the ICAO Engine Emissions Databank

Public aircraft-engine certification data provide a reproducible basis for emissions modeling when proprietary combustor geometry, engine-cycle data, and detailed operating histories are unavailable. This study develops a physically constrained framework based on the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank for modeling gaseous emissions from civil aircraft gas-turbine engines over the landing-and-take-off (LTO) cycle. The analysis uses variables available directly from the ICAO Aircraft Engine Emissions Databank, together with derived LTO quantities, to construct combustor-aware reduced-order correlations for LTO-averaged emission indices of nitrogen oxides (NOx), carbon monoxide (CO), and hydrocarbons (HC), denoted by EINOxLTO, EICOLTO, and EIHCLTO, respectively. High-bypass-ratio (HBPR) turbofan engines are grouped by representative combustor technology, including conventional/single-annular combustor (Conventional/SAC), double-annular combustor (DAC), twin-annular premixing swirler (TAPS), Rolls–Royce TALON lean-burn combustor, low-emissions combustor (LEC), and Unknown categories. For each pollutant and combustor group, two-predictor quadratic response surfaces and power-law correlations are fitted using public engine-level predictors such as overall pressure ratio, bypass ratio, rated thrust, and total LTO fuel consumption. The results show that combustor-aware grouping substantially improves interpretability and that no single global predictor pair represents all pollutants or combustor technologies. For EINOxLTO, overall pressure ratio appears in most selected predictor pairs, consistent with the pressure- and temperature-sensitive nature of NOx formation. For EICOLTO, robust group-specific correlations are obtained for several combustor classes, whereas EIHCLTO is more sensitive to zero and near-zero values, making percentage-based errors and log-space power-law fits less reliable. The quadratic models generally provide stronger within-dataset descriptive fits, while the power-law models provide compact non-negative scaling relations when their errors are acceptable.The proposed framework is suitable for emissions-trend analysis, preliminary comparative assessment, and interpretation of public certification data, but it should not be used as a substitute for certification testing, detailed combustor simulation, or off-design mission-level prediction without additional validation.