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 (NO_x), carbon monoxide (CO), and hydrocarbons (HC), denoted by EINO_xLTO, EICO_LTO, and EIHC_LTO, 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 EINO_xLTO, overall pressure ratio appears in most selected predictor pairs, consistent with the pressure- and temperature-sensitive nature of NO_x formation. For EICO_LTO, robust group-specific correlations are obtained for several combustor classes, whereas EIHC_LTO 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.