The aviation sector faces an urgent mandate to decarbonize while simultaneously limiting the emission of harmful pollutants. This work presents a Mixed-Integer Nonlinear Programming (MINLP) framework for the design of next-generation gas turbine engines, specifically targeting the trade-off between CO₂ reduction and NOx mitigation. The model encompasses advanced architectures featuring Rotating Detonation Combustion (RDC), intercooling, heat recuperation, and three-stream flow management, enabling transitions from conventional Joule–Brayton to complex Joule–Humphrey cycles.

The modeling framework integrates semi-empirical correlations linking local thermodynamic conditions—specifically flame temperature and residence time—to NOx formation rates via the Zeldovich mechanism, while CO₂ emissions are minimized through specific fuel consumption optimization. These emission models are coupled with a rigorous thermodynamic cycle solver, yielding a bi-objective formulation that simultaneously minimizes the two primary environmental contributors over a representative cruise mission, without the confounding influence of weight penalties.

Optimization is performed using a Multi-Start Physics-Acquainted Branch and Bound (MS-PABnB) algorithm, which exploits interdependencies among binary architectural decisions to efficiently navigate the non-convex design space. Pareto fronts are generated for three thrust classes (10 kN, 30 kN, 50 kN), strictly quantifying the environmental cost of disparate architectural choices. Our results demonstrate that Optimized Novel Architecture Engine Designs (ONAEDs) can unlock unique design points where advanced cooling and reheating strategies mitigate the traditional penalty of rising NOx associated with high-efficiency cycles, offering a pathway toward truly sustainable propulsion.