Towards Cleaner Diesel Engines: Performance and Emission Characteristics of Diesel-Ammonia-Methanol Fuel Blends

Abstract

Decarbonization of compression-ignition engines requires evaluation of carbon-free and low-carbon fuel alternatives. Ammonia (NH3) offers zero direct carbon emissions but faces combustion challenges including low flame speed (7 cm/s) and high auto-ignition temperature (657 degrees C). Methanol provides improved reactivity and bound oxygen content that can enhance ignition characteristics. This computational study investigates diesel-ammonia-methanol ternary fuel blends using validated three-dimensional CFD simulations (ANSYS Forte 2023 R2; ANSYS, Inc., Canonsburg, PA, USA) with merged chemical kinetic mechanisms (247 species, 2431 reactions). The model was validated against experimental in-cylinder pressure data with deviations below 5% on a single-cylinder diesel engine (510 cm3, 17.5:1 compression ratio, 1500 rpm). Ammonia energy ratios were systematically varied (10-50%) with methanol substitution levels (0-90%). Fuel preheating at 530 K was employed for high-alcohol compositions exhibiting ignition failure at standard temperature. Results demonstrate that peak cylinder pressures of 130-145 bar are achievable at 10-30% ammonia with M30K-M60K configurations, comparable to baseline diesel (140 bar). Indicated thermal efficiency reaches 38-42% at 30% ammonia-representing 5-8 percentage point improvements over diesel baseline (31%)-but declines to 30-32% at 50% ammonia due to fundamental combustion limitations. CO2 reductions scale approximately linearly with ammonia content: 35-55% at 30% ammonia and 75-78% at 50% ammonia. NOX emissions demonstrate 30-60% reductions at efficiency-optimal configurations. Multi-objective optimization analysis identifies the A30M60K configuration (30% ammonia, 60% methanol, 530 K preheating) as optimal, achieving 42% thermal efficiency, 58% CO2 reduction, 51% NOX reduction, and 11% power enhancement versus diesel. This configuration occupies the Pareto frontier knee point with cross-scenario robustness.