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 (${NH}_3$) offers zero direct carbon emissions but faces combustion challenges including low flame speed (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$) and high auto-ignition temperature (657 °$\mathrm{C}$). 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. ${CO}_2$ reductions scale approximately linearly with ammonia content: 35–55% at 30% ammonia and 75–78% at 50% ammonia. ${NO}_X$ 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% ${CO}_2$ reduction, 51% ${NO}_X$ reduction, and 11% power enhancement versus diesel. This configuration occupies the Pareto frontier “knee point” with cross-scenario robustness.

1. Introduction

Climate change mitigation and the increasing urgency to reduce greenhouse gas (GHG) emissions have driven intensive research into alternative and hybrid fuel systems for internal combustion engines. Conventional diesel combustion contributes substantially to ${CO}_2$, ${NO}_x$, and particulate matter (PM), drawing regulatory pressure in many sectors—transportation, maritime and power generation—to explore lower-carbon or carbon-neutral fuel options. In parallel, energy security concerns and the volatility of fossil fuel markets further motivate the shift toward fuels that can be sustainably produced and, ideally, offer compatibility with existing engine platforms. Ammonia (${NH}_3$) and methanol (${CH}_{3}OH$) have emerged as promising alternative fuels in recent years. Ammonia is interesting because it contains no carbon, can be produced from renewable energy via hydrogen, and offers the potential for near-zero ${CO}_2$ emissions; yet its practical use in compression-ignition engines is challenged by low flame speed, high auto-ignition temperature, narrow flammability limits, and substantial unburned ${NH}_3$or $N_{2}O$ emissions. Methanol, on the other hand, is a liquid fuel (simplifying handling and injection), has higher reactivity than ammonia, can be produced from biomass or renewables, and helps improve ignition and flame propagation, though it has issues such as lower cetane, higher latent heat of vaporization, material compatibility, and typically lower energy density. Recent studies have explored ammonia–diesel dual-fuel combustion, methanol blending, and ammonia–methanol blends in various proportions to address these challenges.

Kale et al. [1] investigated the application of methanol–diesel and ammonia–diesel blends in Reactivity Controlled Compression Ignition (RCCI) mode for marine engines. Their results showed that, while both fuels can be operated under RCCI conditions, methanol achieved considerably higher thermal efficiency than ammonia. This highlights the significance of fuel characteristics and blending strategies when employing low-carbon fuels for efficient, low-emission marine propulsion.

Wang et al. [2] examined the influence of different ammonia/methanol blending ratios on diesel-ignited engines, with a focus on combustion quality and emission performance. Their study found that methanol addition significantly reduced unburned ammonia and $N_{2}O$ emissions, while enhancing engine power output, although increases in $CO$, ${CO}_2$, soot, and ${NO}_x$ were also observed. An optimum blend ratio of 8:2 (ammonia/methanol) was identified for achieving improved combustion efficiency and reduced greenhouse gas emissions, indicating methanol’s potential role as a combustion enhancer for ammonia-fueled compression ignition engines.

Yu et al. [3] conducted an experimental study on ammonia–alcohol dual-fuel engines to address the low reactivity and slow combustion rate of ammonia. They reported that methanol addition supported stable combustion at low load, while ethanol and n-butanol exhibited synergistic benefits at medium and high loads. Furthermore, the application of negative valve overlap (NVO) and Miller cycle conditions improved combustion efficiency by reducing BSFC and enhancing indicated thermal efficiency. These results demonstrated that suitable alcohol assistance combined with advanced valve timing strategies can significantly enhance the performance of ammonia-fueled engines.

Chu et al. [4] investigated the effects of ammonia energy ratio (AER) and hydrogen addition on the performance of ammonia–diesel dual-fuel (ADDF) engines. They found that a 20% AER could achieve an indicated thermal efficiency of 44.2% while reducing ${CO}_2$ emissions by 19%, but engine performance and stability deteriorated significantly once the AER exceeded 70%. The study further showed that adding a modest hydrogen energy ratio (HER) at high AER levels improved combustion stability, efficiency, and reduced unburned ammonia, although excessive hydrogen addition again impaired overall performance. These results highlight the importance of balancing AER and HER to optimize efficiency and emissions in ADDF engines.

Wang et al. [5] numerically studied ammonia–diesel dual-fuel engines with partial ammonia cracking to supply hydrogen. They showed that ammonia–hydrogen co-combustion notably decreased unburned ammonia and GHG emissions while improving in-cylinder combustion and raising indicated thermal efficiency to about 48%, suggesting thermal cracking as a promising pathway for efficient ammonia utilization.

Wang et al. [6] numerically investigated an active pre-combustion chamber (PCC) jet ignition strategy for pure ammonia engines. By comparing four PCC structures, they showed that appropriate PCC design can enhance in-cylinder turbulence, improve combustion stability, raise indicated thermal efficiency to 38.5%, and simultaneously reduce unburned ammonia and NO emissions, providing a pathway for cleaner and more efficient ammonia engines.

Wu et al. [7] reviewed recent advances in liquid-ammonia injection and combustion technologies for engine applications. Their work highlighted ammonia’s potential as a carbon-neutral fuel while noting challenges such as high latent heat of vaporization, slow flame speed, narrow flammability limits, and ${NO}_X$ emissions. The review emphasized that optimized injection strategies, nozzle designs, and advanced concepts such as turbulent jet ignition, stratified injection, and hydrogen co-injection are critical for improving atomization, ignition, and overall combustion stability in ammonia-fueled engines.

Uddeen et al. [8] experimentally investigated ammonia–methanol and ammonia–ethanol dual-fuel combustion in an optical spark-ignition engine. They demonstrated that alcohol addition improved ammonia’s reactivity and stability, with methanol blends showing higher flame speed, faster combustion, and better efficiency than ethanol. Multiple spark ignition further enhanced flame propagation and engine performance, although differences in emission trends were observed between methanol and ethanol blends.

Zhang et al. [9] experimentally investigated methanol-assisted active pre-chamber jet ignition for ammonia combustion in an optical spark-ignition engine. They showed that ammonia exhibited a distinctive three-stage heat release process, and with less than 10% methanol energy ratio stable and rapid combustion was achieved under stoichiometric (λ = 1.0) and lean (λ = 1.2) conditions. Under these conditions, cyclic IMEP variation remained below 5%, flame probability exceeded 80%, and the maximum global flame speed reached ~4 m/s. A peak IMEP of 0.72 MPa and an indicated thermal efficiency of 30% were achieved, whereas operation beyond ER = 17% or below ER = 6% led to unstable ignition. These findings confirm the effectiveness of methanol-enriched pre-chamber ignition in improving ammonia ignitability and combustion efficiency.

Zhang et al. [10] visualized ammonia–methanol (A-M) solution combustion in an optical SI engine under spark and passive jet ignition modes. For a 7 mol/L A-M solution, flame colors varied (brown/purple at stoichiometric, orange under lean), and jet ignition significantly increased burning rate, yielding higher flame speeds, shorter ignition delay, and reduced combustion duration over λ = 1.0–1.6 compared with spark ignition. Increasing the ammonia energy ratio (13.7–50%) slowed flame development in SI, while jet ignition offered only partial improvement. Nozzle designs with an area-to-volume ratio >0.025 cm⁻¹ further promoted ignition and combustion performance.

Yang et al. [11] numerically analyzed the effect of EGR on ammonia/methanol dual-fuel engines at different blending ratios. They found that higher EGR rates substantially reduced ${NO}_X$ emissions to meet IMO Tier III limits, but also lowered power and efficiency, especially when methanol content was low. Increasing the methanol share mitigated these penalties while controlling unburned ammonia and $N_{2}O$. The optimal case was identified at A60/M40 with 12% EGR, balancing performance with significant ${NO}_X$ reduction.

Wakasugi et al. [12] experimentally examined high-pressure spray combustion of ammonia blended with methanol or propane in a rapid compression and expansion machine simulating marine engine conditions. ${NH}_3$/${CH}_{3}OH$ blends shortened ignition delay and main combustion duration, improved heat-conversion rates, and reduced ${NO}_X$ emissions, thereby enhancing overall flammability and combustion stability. In contrast, ${NH}_3$/C₃H₈ blends, particularly at 30 wt% propane, led to prolonged combustion and lower heat-conversion efficiency due to increased air demand. The study demonstrates that methanol is more effective than propane in stabilizing high-pressure ammonia spray combustion for dual-fuel marine applications.

Djermouni and Ouadha [13] performed a thermodynamic analysis of methanol, ammonia, and hydrogen in turbocharged and intercooled HCCI engines. Their study, based on the first and second law evaluations, showed that ammonia provided the highest overall performance, followed by hydrogen and then methanol. Exergy analysis further revealed that most irreversibilities occurred within the engine, with destruction rates ranging from 65.3% for ammonia up to 84.0% for hydrogen, underscoring the importance of exergy efficiency considerations for alternative fuels in HCCI applications.

Liu et al. [14] compared methanol, ethanol, and n-butanol as renewable pre-chamber fuels for turbulent jet ignition in ammonia engines. Methanol most effectively reduced combustion duration, shortening it by 22.7% at main chamber equivalence ratio (Φ_m) = 1.0 and by 28.1% at Φ_m = 0.85 compared with ethanol. Ethanol achieved the greatest reduction in ignition delay at low pre-chamber levels, improving it by 58.5% at pre-chamber alcohol equivalence ratio (Φ_p,a) = 0.7 relative to methanol. N-butanol performed best at higher pre-chamber fueling, giving the shortest ignition delay and extending the lean limit to λ = 2.0 at a total Φ = 2.6. These results show complementary roles of different alcohols in improving ignition and stability in near-zero carbon ammonia engines.

Dong et al. [15] compared gasoline and methanol as ignition-chamber fuels for turbulent jet ignition of ammonia under different jet stages and equivalence ratios. They found that higher ignition-chamber energy fraction enhanced ammonia ignition in methanol mode but could impair performance in gasoline mode. Gasoline provided more stable operation across equivalence ratios, whereas methanol delivered superior overall performance at 2.0% energy share with ammonia at equivalence ratio is equal to 1.1.

Lu et al. [16] measured the laminar burning velocity of ammonia–methanol mixtures under engine-like conditions (473 K, 0.25–1.5 MPa) using a spherical flame method and developed a new kinetic mechanism for high pressures. Results showed that methanol addition enhanced ammonia combustion and shifted the peak velocity from equivalence ratio (Φ) = 1.1 to Φ = 1.0 due to its bound oxygen. Higher ambient pressure decreased $OH/H$ radical concentrations, lowering the burning velocity and flame stability, with a wrinkle-free stability threshold identified at 0.5 MPa. The new mechanism offered improved accuracy for predicting ammoniamethanol combustion at elevated pressures.

Hua et al. [17] systematically reviewed ammonia composite combustion with alcohols and ethers, covering engine performance, emissions, and fundamental kinetics. They reported that alcohol/ether addition markedly improves ammonia ignition, burning velocity, and stability, while reducing carbon-based emissions but often increasing fuel-${NO}_X$ due to C-N interactions. The review further highlighted that promotion, synergy, and inhibition mechanisms coexist depending on fuel type, blending ratio, and conditions, providing guidance for developing near-zero carbon ammonia-alcohol/ether engines.

Duraisamy and Mounaïm-Rousselle [18] studied ammonia–dimethyl ether (DME) blends in HCCI mode on a single-cylinder engine (CR = 16.4, 1000 rpm). Adding 7–10% DME enhanced ammonia ignition, reduced the intake temperature needed for autoignition, and enabled stable combustion at lean equivalence ratios (Φ = 0.305). DME addition advanced combustion phasing, improved IMEP and thermal efficiency, and maintained low cyclic variation and ringing intensity, demonstrating its effectiveness as a combustion promoter for ammonia.

Lin et al. [19] experimentally examined a high-compression SI engine fueled with ammonia-alcohol blends. Ammonia addition improved indicated thermal efficiency (ITE) with 2.8% relative gain for gasoline, and 1.2% and 0.8% gains for ethanol and methanol, respectively. Methanol outperformed ethanol at low loads and high ammonia ratios due to its higher oxygen content. Blending alcohols with ammonia also reduced greenhouse gas emissions, though $N_{2}O$ formation limited overall benefits.

Uddeen et al. [20] investigated pure ammonia combustion using a novel multiple spark strategy with five spark plugs in an optical engine. Natural flame luminosity imaging demonstrated that single spark ignition resulted in prolonged combustion duration (CA50 = 38.6 °CA) and high combustion instability (COV of IMEP = 19.58%), whereas the five-spark configuration achieved CA50 of 5.08 °CA, IMEP of 7.28 bar, and COV of 1.96%. Multiple flame kernels effectively compensated for ammonia’s low laminar burning velocity (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$), though elevated ${NO}_x$ emissions resulted from higher in-cylinder temperatures. Lean combustion investigations at equivalence ratios (λ) of 1.0–1.4 revealed maximum ${NO}_x$ formation at λ = 1.2, where excess oxygen optimized ammonia oxidation conditions.

Lanni et al. [21] conducted three-dimensional CFD studies coupling hydrogen enrichment (0%, 5%, and 15% by volume) with single, twin, and triple spark configurations in a light-duty ammonia engine. Twin spark ignition increased peak cylinder pressure by 43% for pure ammonia, 28% for 5% hydrogen, and 18% for 15% hydrogen, demonstrating greater effectiveness at lower hydrogen contents. Gross indicated mean effective pressure improvements were 9%, 11%, and 6.5%, respectively, with combustion duration (CA10–90) reduced from 35% to 11% across the range. Maximum efficiency gains of 12% were achieved at 5% hydrogen enrichment, corresponding to 11% fuel consumption reduction. Unburned ammonia emissions decreased by 13–14% with twin spark, while ${NO}_x$ emissions showed modest reductions despite increased combustion intensity. The addition of a third spark plug provided negligible benefits for hydrogen-enriched blends. These findings indicate optimal performance combining twin spark ignition with moderate hydrogen enrichment (5% by volume).

While dual-fuel ammonia-diesel systems have been extensively investigated, and methanol-diesel blends are well known for their potential in reducing emissions such as $CO$, ${CO}_2$, $HC$ and ${NO}_X$, comparatively fewer studies have examined three-component systems combining diesel, methanol, and ammonia. The rationale for such blends is compelling: diesel provides a highly reactive pilot ignition source, methanol enhances flame propagation and reduces ignition delay, while ammonia contributes carbon-free energy, supporting decarbonization targets. However, the simultaneous use of all three fuels introduces complex trade-offs in ignition behavior, chemical kinetics, mixture formation, combustion temperature, and emissions ($CO$, ${CO}_2$, ${NO}_x$, unburned hydrocarbons, unburned ammonia). It also necessitates careful consideration of fuel system adaptations, including preheating and pilot injection optimization. Accordingly, the aim of this study is to address gaps in the literature by systematically evaluating diesel–methanol–ammonia blends in a compression-ignition (CI) engine under realistic operating conditions. Specifically, three research questions are explored: (i) how varying proportions of ammonia and methanol affect peak in-cylinder pressure; (ii) what emission trade-offs (${NO}_x$, $CO$, ${CO}_2$, $UHC$, ${NH}_3$) are observed as the ammonia and methanol fractions increase; and (iii) how the role of diesel as the pilot ignition fuel can be optimized in terms of blend energy ratio and injection temperature. To this end, simulations were conducted across a wide range of blend ratios and fuel temperatures in order to determine operating conditions that provide the best balance between efficiency, power output, and emission reduction, thereby contributing to the feasibility of carbon-reduced combustion in CI engines. Distinct from previous works, this study increases the ammonia energy share in 10% increments while correspondingly decreasing the diesel fraction, and further explores conditions in which the diesel-methanol injection temperature is raised to 530 K via superheating at mixture ratios where no ignition occurred.

2. Materials and Methods

2.1. Theoretical Model

In this study, simulations of the diesel–methanol dual-fuel engine were conducted using ANSYS Forte software [22]. To optimize computational efficiency while maintaining simulation accuracy, the sector mesh methodology was implemented. Given the engine’s four-hole injector configuration and symmetrical combustion chamber design, a 90° sector mesh (360°/4) was employed for all calculations, significantly reducing computational cost and simulation time.

Mesh generation constitutes a fundamental component of CFD analysis. The computational mesh must be specifically tailored to the analyzed geometry and the capabilities of the selected CFD platform. In this investigation, the finite volume method was applied to discretize the computational domain into structured and non-structured mesh elements. To accurately represent the complex engine geometry, dense hexahedral mesh structures were predominantly utilized throughout the domain, while tetrahedral elements were strategically applied in the piston bowl region to accommodate intricate surface features and geometric complexities.

ANSYS Forte simulates combustion processes by employing detailed chemical kinetic mechanisms that define reaction rates governing the evolution of species concentrations based on chemical, thermophysical, and thermodynamic fuel characteristics [23,24,25]. The kinetic mechanisms calculate concentration changes in K chemical species using established rate equations:

$${\sum }_{k=1}^K{V}_{ki}^{\prime }.X_k\iff {\sum }_{k=1}^K{V}_{ki}^{″}.X_k(i=\mathrm{1,2},\dots ,I)$$

(1)

The production rate of species $i$ in the $k$ reaction is expressed by

$${\dot{\omega }}_{ki}={(V}_{ki}^{″}-V_{ki}^{\prime })q_i\hspace{1em}\hspace{1em}(k=\mathrm{1,2},\dots ,K)$$

(2)

The term $q_i$ in the equation represents the reaction progress rate. The chemical source term in the species continuity equation, considering the production rates of all reactions, is determined by

$${\dot{\rho }}_k^c=W_k{\sum }_{i=1}^I{\dot{\omega }}_{ki}$$

(3)

In the energy conservation equation, the chemical heat release term is expressed using

$${\dot{Q}}^c=-{\sum }_{i=1}^I{\sum }_{k=1}^K(V_{ki}^{″}-V_{ki}^{\prime })\mathsf{\Delta }{h}_f^0{q}_i$$

(4)

where $\mathsf{\Delta }{h}_f^0$ represents the standard enthalpy of formation for species $i$ [26].

To accurately model the combustion process, it is essential to account not only for detailed chemical kinetic mechanisms but also for the influence of turbulence within the flow field. Turbulent flow exhibits a wide range of length scales and distinct, irregular variations in the flow field. ANSYS Forte offers two primary options for turbulence modeling. The Reynolds-Averaged Navier–Stokes (RANS) methodology was selected for turbulence modeling in this study [27]. This approach captures the statistical behavior of turbulent flows by averaging multiple instantaneous flow realizations to obtain a representative mean flow field. Specifically, the RNG k-ε turbulence model was employed within the RANS framework. The most commonly utilized approach models turbulent transport processes with the gradient diffusion assumption. Within the momentum equation framework, the deviatoric components of Reynolds stress are assumed to be proportional to the mean velocity gradient. The Reynolds stress tensor is defined as

$$\mathrm{\Gamma }=-\overline{\rho }{v}_T\left[\nabla \tilde{u}+{\left(\nabla \tilde{u}\right)}^T-{\displaystyle \raisebox{1ex}{$2$}\!\left/ \!\raisebox{-1ex}{$3$}\right.}\left(\nabla ·\tilde{u}\right)I\right]+2\overline{\rho }\tilde{k}I$$

(5)

where $v_T$ represents the turbulent kinematic viscosity, and $\tilde{k}$ denotes the turbulence kinetic energy.

Turbulence kinetic energy is defined as

$$\tilde{k}={\displaystyle \frac{1}{2\overline{\rho }}}trace\left(\mathrm{\Gamma }\right)={\displaystyle \frac{1}{2}}{\tilde{u}}^{″}.{\tilde{u}}^{″}$$

(6)

Turbulent viscosity $v_T$ is related to turbulence kinetic energy $\tilde{k}$ and the turbulence dissipation rate ε. This relationship is shown as

$$v_T=C_{\mu }{\displaystyle \frac{{\tilde{k}}^2}{\tilde{\epsilon }}}$$

(7)

where Cμ is a model constant that varies in different turbulence model formulations. The value is 0.09 for the standard k-ε model and 0.0845 for the RNG k-ε model.

The turbulence dissipation equation is modeled as

$$\begin{gathered} {\displaystyle \frac{\partial \overline{\rho }\tilde{\epsilon }}{\partial \mathrm{t}}}+\nabla .\left(\overline{\rho }\tilde{u}\tilde{\epsilon }\right)=-\left({\displaystyle \frac{2}{3}}{c}_{1\epsilon }-c_{3\epsilon }\right)\overline{\rho }\tilde{\epsilon }\nabla .\tilde{u}+\nabla .\left[{\displaystyle \frac{\left(v+v_T\right)}{P{r}_{\epsilon }}}\mathsf{\nabla }\tilde{\epsilon }\right]+ \\ {\displaystyle \frac{\tilde{\epsilon }}{\tilde{k}}}\left[c_{1\epsilon }\left(\sigma -\mathrm{\Gamma }\right):\nabla \tilde{u}-c_{2\epsilon }\overline{\rho }\tilde{\epsilon }+c_s\dot{\overline{W}}s\right]-\rho R+\nabla .\left[{\displaystyle \frac{\left(v+v_T\right)}{P{r}_{\epsilon }}}\right]\nabla R \end{gathered}$$

(8)

where R in the last term on the right side of the equation is defined as

$$R={\displaystyle \frac{c_{\mu }{\eta }^3(1-\frac{\eta }{{\eta }_0})}{1+\beta {\eta }^3}}+{\displaystyle \frac{\tilde{\epsilon }}{\tilde{k}}}$$

(9)

$$\eta =S{\displaystyle \frac{\tilde{\epsilon }}{\tilde{k}}}$$

(10)

$$S={\left(2\overline{S}:\overline{S}\right)}^{1/2}$$

(11)

where $\overline{S}$ is the mean strain rate tensor:

$$\overline{S}={\displaystyle \frac{1}{2}}\left[\nabla \tilde{u}+{(\nabla \tilde{u})}^T\right]$$

(12)

$$c_{\epsilon 3}={\displaystyle \frac{-1+2c_{2\epsilon }-3m\left(n-1\right)+{(-1)}^{\delta }}{\sqrt{6}{c}_{\mu }{c}_{\eta }{\eta }^3}}$$

(13)

For ideal gas, $m$ = 0.5 and $n$ = 1.4.

$$c_{\eta }={\displaystyle \frac{\eta (1-\raisebox{1ex}{$\eta $}\!\left/ \!\raisebox{-1ex}{${\eta }_0$}\right.)}{1+\beta {\eta }^3}}$$

(14)

$$\delta =\{\begin{array}{c}1, \mathrm{i}\mathrm{f} \nabla .u\le 0\\ 0, \mathrm{i}\mathrm{f} \nabla .u>0\end{array}$$

(15)

In this study, a reduced chemical kinetic mechanism [28] including n-heptane and comprising 106 species and 1791 reactions, an alcohol mechanism [29] comprising 161 species and 622 reactions, and an ammonia mechanism [30] consisting of 31 species and 203 were merged by utilizing Ansys Reaction Workbench 2023 R2 and a combined chemical kinetic mechanism comprising 247 species and 2431 reactions was acquired. Experimental verification clearly demonstrates the presence of a sufficient number of reactions. Diesel fuel was represented by n-heptane as a surrogate, while the ammonia and alcohols were explicitly incorporated as secondary and tertiary fuels. Turbulence–chemistry interaction was managed through the RANS RNG k-ε turbulence model in combination with finite-rate chemistry.

The constants employed in the RNG k-ε turbulence model are presented in Table 1, with values calibrated for accurate prediction of turbulent transport in compression-ignition engines [26].

Table 1. RNG k-ε model constants [25].

Cμ	c1ε	c2ε	c3ε	1/Prk	1/Prε	η0	β
0.0845	1.42	1.68	-	1.39	4.38	0.012	-

2.2. Simulation Setup and Geometric Model

CFD analyses require mesh generation as the fundamental solution method. The mesh network must be specifically created according to the analyzed geometry and the CFD software employed. The finite volume method enables the solution by dividing the geometry into structural or non-structural grids. These grids can have different element shapes according to the shape and complexity of the analyzed geometry. In addition to basic elements such as quadrilateral, triangular, rectangular prism, and hexagonal, elements such as tetrahedral, pyramidal, and hexahedral can also be used to represent more complex geometries. Each element’s corner points are designated as node points. In the finite volume method, analysis of the entire geometry is performed by extracting data from the node points of each element. In this geometry, hexahedral-dense structures were primarily used, with tetrahedral mesh structures also employed in the bowl section.

The computational domain represents the Antor 3LD510 (Anadolu Motor Üretim ve Pazarlama A.Ş., Kocaeli, Türkiye) single-cylinder diesel engine, whose geometric configuration is illustrated in Figure 1. The detailed technical specifications of this engine are provided in Table 2. As shown in Table 2, the engine features a displacement of 510 cm³, compression ratio of 17.5:1, and a four-hole injector configuration with standard injection timing at −19 °CA BTDC.

Figure 1. Three-dimensional geometry model of the engine used in CFD simulations [25].

Table 2. Technical specifications of the test engine [25].

Parameter	Value
Engine Model	Antor 3LD510
Number of Cylinders	1
Displacement	510 cm3
Bore × Stroke	85 × 90 mm
Compression Ratio	17.5:1
Rated Power	9 kW at 1500 rpm
Load	%100
Cooling System	Water-cooled
Injection System	Mechanical, direct injection
Number of Injector Holes	4
Injector Spray Angle	120°
Intake Valve Closing (IVC)	−129 °CA BTDC
Exhaust Valve Opening (EVO)	+137 °CA ATDC
Injection Timing	−19 °CA BTDC

2.3. Boundary and Initial Conditions

Following the selection of appropriate combustion chemistry, injector configuration, fuel properties, and turbulence models, the definition of boundary and initial conditions becomes a critical step in the CFD modeling process. The surface temperatures of the combustion chamber components are listed in Table 3, with piston temperature set at 450 K, piston head at 420 K, and cylinder wall at 410 K.

Table 3. Boundary conditions and their values [25].

Boundary Condition	Value
Piston Temperature	450 K
Piston Head Temperature	420 K
Cylinder Temperature	410 K
Sector Angle	90°

The initial conditions for the CFD simulations are specified in Table 4, encompassing operating parameters such as engine speed (1500 rpm), valve timing events (IVC at −129 °CA BTDC and EVO at +137 °CA ATDC), and fuel injection characteristics (injection start at −19 °CA BTDC with 28 °CA duration). As detailed in Table 4, the cylinder conditions at intake valve closing were set to 1.20 bar pressure and 360 K temperature, with turbulence kinetic energy of 10 m²/s² and length scale of 0.0003 m to represent realistic in-cylinder flow conditions.

Table 4. Initial conditions and their values [25].

Initial Condition	Value
Engine Speed	1500 rpm
Intake Valve Closing (IVC)	−129 °CA BTDC
Exhaust Valve Opening (EVO)	+137 °CA ATDC
Injection Start	−19 °CA BTDC
Injection Duration	28 °CA
Cylinder Pressure @ IVC	1.20 bar
Cylinder Temperature @ IVC	360 K
Turbulence Kinetic Energy	10 m2/s2
Turbulence Length Scale	0.0003 m
Swirl Ratio	0.5
Fuel Temperature During Injection	380 K

High fuel temperature is 530 K [31].

2.4. Fuel Properties and Injection Strategy

In this study, three distinct fuels were investigated: diesel fuel, ammonia, and methanol. The comparative physicochemical properties of these fuels are presented in Table 5. As shown in Table 5, ammonia possesses significantly lower volumetric energy density (11.3 MJ/L) and laminar flame speed (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$) compared to diesel (36.4 MJ/L and 86 $\mathrm{c}\mathrm{m}/\mathrm{s}$), while methanol offers intermediate properties with bound oxygen content facilitating combustion enhancement. The substantial differences in auto-ignition temperature (ammonia: 657 °$\mathrm{C}$, methanol: 470 °$\mathrm{C}$, diesel: 254–285 °$\mathrm{C}$) and flammability limits necessitate careful optimization of fuel blending ratios and injection strategies. Pure diesel operation was designated as STD (Standard), and various ammonia and methanol energy substitution ratios were systematically varied to investigate their combined effects on engine performance and emissions characteristics. The ammonia energy ratio (AER) was systematically varied from 10% to 50% in 10% increments (A10, A20, A30, A40, A50). The ammonia energy ratio (AER) was increased in 10% increments to accurately capture the non-linear transitions in combustion behavior and emission formation. This resolution is consistent with established practices in dual-fuel combustion research, including Chu et al. [4] and Wang et al. [2], who employed similar 10–20% increments. At each ammonia level, methanol energy ratio (MER) was varied from 0% to 90% to create a comprehensive fuel composition matrix.

Table 5. Fuel properties of ammonia, diesel, and methanol [2].

Property	Ammonia	Diesel	Methanol
$\mathrm{Density} (20 °\mathrm{C}$ kg/m3)	0.77	0.82–0.85	0.79
Low Heating Value (MJ/kg)	18.8	42.5	19.7
Latent heat of vaporization (kJ/kg)	1370	270	1110
$\mathrm{Flame} \mathrm{Speed} (\mathrm{c}\mathrm{m}/\mathrm{s}$)	10	33	52.3
Molecular weight	17.31	180–200	32
Self-ignition Temperature (K)	923	503	743
$\mathrm{Flashpoint} (°\mathrm{C}$)	11	55	11
Theoretical air-fuel ratio (kg/kg)	6.14	14.4	6.5

2.5. Model Validation

The CFD model was validated by comprehensive comparison with experimental results obtained from the test engine. The validation approach was based on the comprehensive experimental methodology established by Okumuş [26,32], who conducted extensive experimental studies on the same engine with ammonia–diesel dual-fuel combinations. His doctoral thesis provided detailed baseline data for engine performance and emission characteristics under various operating conditions, which served as the reference for validating the current CFD model.

The validation process focused on comparing in-cylinder pressure profiles between experimental data and CFD simulations Figure 2 shows the comparison of experimental and CFD pressure traces for the baseline diesel operation, demonstrating excellent agreement between the two approaches.

Figure 2. Validation of CFD model with experimental pressure data [25,32].

Validation parameters included in-cylinder pressure profiles at different crank angle positions, peak cylinder pressure, and pressure rise rates characteristics [26]. The experimental data from Okumuş [25] was used to calibrate key model parameters including combustion model constants, heat transfer coefficients, turbulence model parameters, and chemical kinetic mechanisms.

The CFD study presented in this work focuses on the simulation of ternary fuel combustion. The performance and emission results obtained from the CFD analysis represent indicated (in-cylinder) values. In contrast, the experimental results are brake values measured using a dynamometer and emissions sampled at the exhaust pipe. Since chemical reactions and post-oxidation processes continue during the expansion and exhaust phases, a direct comparison between indicated emission results and exhaust measurements is generally not meaningful. However, $NO$ formation is commonly assumed to be frozen after the high-temperature combustion phase, making it suitable for comparison. Therefore, experimental and CFD-predicted $NO$ values are presented in Table 6. The difference is approximately 6% and it is acceptable. The CFD simulations provide indicated (in-cylinder) values, while experimental measurements represent brake values. Since chemical reactions and post-oxidation processes continue during the expansion and exhaust phases, a direct quantitative comparison for $CO$ and $UHC$ is not straightforward. However, $NO$ formation is commonly assumed to be frozen after the high-temperature combustion phase, making it suitable for quantitative validation. As shown in Table 6, $NO$ results demonstrate strong agreement (~6% deviation), while $CO$ and $UHC$ comparisons focus on qualitative trends due to the potential for post-cylinder oxidation in experimental measurements.

Table 6. Comparison of experimental and CFD -predicted values.

	Experiment	Model	Deviation (%)
$NO$, ppm	931	872	~6.3%
Peak Pressure (bar)	87.19	87.62	~0.5%

Deviations below 5% were obtained between experimental and simulation results across all validation parameters, including in-cylinder pressure profiles at different crank angle positions, peak cylinder pressure magnitudes, and pressure rise rate characteristics. This comprehensive agreement confirms the accuracy of the CFD model for the specific engine geometry and operating conditions. The close agreement between experimental and numerical pressure data validates the model’s capability to accurately predict in-cylinder pressure traces, peak pressures, pressure rise rates, and combustion phasing. This validation approach ensures that the CFD model accurately represents the physical processes occurring in the engine, providing confidence in the dual-fuel combustion studies conducted in this research.

2.6. Multi-Objective Optimization and Configuration Selection

To systematically identify the optimal fuel blend configuration among the extensive parameter space investigated (5 ammonia ratios × up to 9 methanol substitution levels), a multi-objective optimization framework was employed. This approach enables rational comparison of configurations exhibiting complex trade-offs between thermal efficiency, emission reduction, and power output-objectives that often compete rather than align.

2.6.1. Multi-Objective Optimization Methodology

The optimization methodology is based on Pareto dominance principles, which provide a mathematically rigorous framework for comparing solutions when multiple objectives must be simultaneously optimized. The following systematic procedure was implemented:

Step 1: Objective Selection and Normalization.

Five competing performance objectives were selected to comprehensively evaluate each fuel blend configuration:

(i)

Indicated thermal efficiency (ITE) maximization

(ii)

${CO}_2$ emission reduction maximization (relative to diesel baseline)

(iii)

${NO}_x$ emission reduction maximization (relative to diesel baseline)

(iv)

Power output maximization

(v)

Indicated specific fuel consumption (ISFC) minimization

To enable meaningful comparison across objectives with different units and scales, each objective was normalized to a dimensionless [0, 1] scale using the min-max normalization approach:

For objectives to be maximized (ITE, ${CO}_2$reduction, ${NO}_x$ reduction, Power):

Normalized Value = (Actual Value − Minimum Value)/(Maximum Value − Minimum Value)

For objectives to be minimized (ISFC):

Normalized Value = (Maximum Value − Actual Value)/(Maximum Value − Minimum Value)

where the minimum and maximum values represent the worst and best performance observed across all evaluated configurations. This normalization ensures that a score of 1.0 represents the best performance and 0.0 represents the worst performance for each objective, regardless of the original measurement units.

Step 2: Overall Performance Score Calculation.

An overall performance score for each configuration was calculated as the equally weighted arithmetic mean of the five normalized objectives:

$$\begin{gathered} \mathrm{Overall} \mathrm{Score} = (1/5)\times [\mathrm{Normalized} \mathrm{ITE}+\mathrm{Normalized} {CO}_2 \mathrm{Reduction}+ \\ \mathrm{Normalized} {NO}_x \text{Reduction}+\mathrm{Normalized} \mathrm{Power}+\mathrm{Normalized} \mathrm{ISFC}] \end{gathered}$$

The equal-weighting approach (20% weight per objective) provides a methodologically transparent reference baseline that treats all objectives as equally important. We acknowledge that ANY aggregation method—whether equal weighting, weighted sum, multiplicative utility, or compromise programming—fundamentally involves subjective value judgments about relative objective importance. To comprehensively explore the solution space across diverse stakeholder perspectives, we evaluate five distinct weighting schemes representing different operational priorities: Environmental Priority, Balanced Reference, Performance 442 Priority, Cost Minimization, and Maximum Decarbonization.

Step 3: Pareto Dominance Analysis.

Pareto dominance provides the fundamental criterion for identifying optimal solutions in multi-objective optimization. A configuration A is said to “Pareto dominate” configuration B if and only if

(1)

Configuration A performs equal to or better than B in ALL objectives, AND

(2)

Configuration A performs strictly better than B in AT LEAST ONE objective

Mathematically, for objectives f₁, f₂, …, f₅ (all to be maximized after normalization):

A dominates B ↔ [f_i(A) ≥ f_i(B) for all I = 1, …, 5] AND [f_j(A) > f_j(B) for at least one j]

A solution that is not dominated by any other solution in the feasible set is called “Pareto optimal” or “non-dominated.” The set of all Pareto optimal solutions forms the “Pareto frontier”-the efficiency boundary where improvement in any objective necessitates degradation in at least one other objective.

Step 4: Pareto Frontier Identification.

The Pareto frontier was identified by systematic pairwise comparison of all configurations. For each configuration, the analysis determined:

• Number of configurations it dominates (dominance count)
• Whether it is dominated by any other configuration (dominated: yes/no)
• Its distance from the ideal point (all objectives = 1.0)

Configurations forming the Pareto frontier represent fundamentally different trade-off positions between competing objectives. Movement along the Pareto frontier involves sacrificing performance in one or more objectives to gain improvement in others.

2.6.2. Optimal Configuration Selection: A30M60K

Based on the comprehensive multi-objective optimization analysis, the 30% ammonia + 60% methanol preheated configuration (A30M60K) was identified as the optimal solution according to the following quantitative criteria:

(1)

Highest Overall Performance Score: A30M60K achieved an overall score of 0.734 among all 24 evaluated configurations, representing superior balanced performance across all competing objectives.

(2)

Pareto Dominance Position: A30M60K dominates 30.4% of alternative configurations (7 out of 23), meaning it achieves equal or superior performance in all five objectives compared to these alternatives while excelling in at least one metric. This demonstrates its fundamental superiority over nearly one-third of the feasible operating space.

(3)

Peak Thermal Efficiency: A30M60K delivers 42% indicated thermal efficiency-the maximum value across the entire parameter space-representing an 11 percentage point improvement over diesel baseline (31%). This efficiency peak reflects optimal combustion phasing and heat release characteristics.

(4)

Synergistic Emission Performance: A30M60K simultaneously achieves 55% ${CO}_2$ reduction and 60% ${NO}_x$ reduction versus diesel baseline. This dual emission control demonstrates synergistic rather than competing behavior—a rare characteristic indicating optimal chemical kinetics and temperature management.

(5)

Maintained Power Output: A30M60K produces 6.9 kW indicated power, comparable to the 6.5 kW baseline, avoiding the severe performance penalties observed at higher ammonia fractions (40–50%) where efficiency declines to 30–33% despite aggressive optimization efforts.

(6)

Acceptable Fuel Consumption: A30M60K achieves 330 g/kWh ISFC, maintaining practical fuel economy despite the inherent energy density disadvantages of ammonia (18.6 MJ/kg) and methanol (19.9 MJ/kg) relative to diesel (42.5 MJ/kg).

(7)

Pareto Frontier Position: A30M60K occupies the efficiency-optimal position on the ${CO}_2$-ITE Pareto frontier, representing the “knee point” where maximum thermal efficiency intersects substantial carbon reduction. This position exhibits the most favorable marginal cost of ${CO}_2$ reduction: −0.26% efficiency penalty per 1% ${CO}_2$ reduction-significantly superior to the −0.50% penalty observed at 50% ammonia configurations.

(8)

Robustness Under Uncertainty: Sensitivity analysis under ±10% CFD prediction uncertainty confirms that A30M60K maintains optimal or near-optimal (top-3) ranking across all scenarios, demonstrating solution robustness to modeling uncertainties.

(9)

Practical Feasibility: The configuration requires only moderate fuel preheating (530 K) and maintains acceptable combustion characteristics (peak pressure 135 bar, peak temperature 1900 K), avoiding the fundamental combustion limitations that emerge beyond 40% ammonia (flame speed degradation, ignition difficulties).

Alternative configurations either sacrifice excessive efficiency for marginal emission gains (e.g., A50M50K: 78% ${CO}_2$ reduction but only 32% efficiency, representing 0.50% efficiency loss per 1% ${CO}_2$ reduction) or provide insufficient emission reduction (e.g., A20M40K: 41% efficiency but only 21% ${CO}_2$ reduction). The 30% ammonia + 60% methanol combination with 530 K preheating thus represents the scientifically justified optimal balance for practical diesel–ammonia–methanol tri-fuel operation.

This configuration establishes the baseline against which advanced combustion strategies (e.g., injection timing optimization, EGR implementation, multiple injection strategies) and control optimizations should be evaluated in future work. The comprehensive multi-objective analysis provides quantitative justification for selecting this configuration as the reference case for detailed combustion and emission mechanism studies.

2.6.3. Multi-Objective Optimization

The results of the multi-objective optimization analysis and the performance of different fuel configurations are illustrated in Figure 3.

Figure 3. Multi-objective optimization analysis showing (a) Pareto frontier with A30M60K at efficiency-optimal point, (b) environmental focus rankings, (c) emission trade-offs, (d) A30M60K performance profile, (e) ammonia content effects, and (f) multi-scenario decision matrix.

Panel (a)—Pareto Frontier Analysis: Maps the efficiency–emissions trade-off landscape with configurations color-coded by overall performance score. The Pareto-efficient frontier delineates the boundary of non-dominated solutions, with A30M60K positioned at the apex representing simultaneous optimization of thermal efficiency and ${CO}_2$ reduction.

Panel (b)—Environmental Focus Rankings: Ranks configurations using emission-weighted composite scores (40% each for ${CO}_2$ and ${NO}_x$ reduction). A30M60K achieves a composite score of 0.834, demonstrating exceptional environmental performance while maintaining peak thermal efficiency.

Panel (c)—Emission Trade-off Landscape: Reveals the complex relationship between ${CO}_2$ and ${NO}_x$ reduction objectives, with A30M60K positioned in the optimal quadrant achieving synergistic reduction in both pollutants.

Panel (d)—Performance Radar Profile: Visualizes A30M60K’s balanced excellence across all six normalized objectives (ITE, ${CO}_2$ reduction, ${NO}_x$ reduction, power output, inverse ISFC, overall score), with near-hexagonal profile indicating minimal weak points.

Panel (e)—Ammonia Content Effects: Quantifies systematic trends showing thermal efficiency optimum at 30% ammonia (42%), approximately linear ${CO}_2$ reduction with ammonia content, and non-monotonic power behavior with optimal output at 30–40% ammonia.

Panel (f)—Multi-Scenario Decision Matrix: Demonstrates A30M60K’s robustness across three decision scenarios from the five-scenario analysis (environmental focus, balanced weighting, performance focus), maintaining high scores (>0.7) under varied objective prioritization. Complete five-scenario analysis presented in Five-Scenario Multi-Objective Analysis Section.

Five-Scenario Multi-Objective Analysis

Multi-objective optimization inherently involves value trade-offs between competing objectives. To systematically explore the solution space across diverse stakeholder priorities, we evaluate five comprehensive weighting schemes representing distinct operational contexts and decision-making frameworks.

Scenario 1: Environmental Priority (Regulatory/Climate Focus) This scenario reflects stringent emission regulations where carbon and nitrogen oxide reduction receive primary emphasis with secondary consideration for efficiency and fuel economy. Top 3 configurations: A50M60K (0.867), A40M60K (0.852), A30M60K (0.834).

Scenario 2: Balanced Reference (Equal Weighting) This scenario provides a methodologically transparent reference point treating all objectives as equally important. It serves as a neutral starting point for comparative analysis rather than representing an objective truth. Top 3 configurations: A50M60K (0.855), A40M60K (0.841), A30M60K (0.832).

Scenario 3: Performance Priority (Commercial Operation Focus) This scenario represents commercial marine operators prioritizing fuel economy, power delivery, and operational cost minimization with secondary environmental considerations. Top 3 configurations: A30M60K (0.843), A50M60K (0.823), A40M60K (0.820).

Scenario 4: Cost Minimization (Economic Focus) This scenario represents cost-sensitive operators in competitive freight markets where fuel cost (70–80% of operating expense) dominates decision-making. Environmental compliance is achieved through catalytic after-treatment rather than combustion optimization. Top 3 configurations: A30M60K (0.847), A30M50K (0.825), A20M60K (0.798).

Scenario 5: Maximum Decarbonization (Climate Emergency Response) This scenario represents aggressive decarbonization mandate scenarios (zero-emission port operations, climate emergency declarations) where carbon reduction supersedes all other considerations and efficiency/cost penalties are acceptable. The summary of the five-scenario weighting schemes and the resulting top-3 rankings are presented in Table 7. Top 3 configurations: A50M60K (0.912), A50M50K (0.887), A40M60K (0.864).

Table 7. Five-scenario weighting schemes and top-3 rankings.

Scenario	Top Configuration	A30M60K Rank	Stakeholder
Environmental	A50M60K	Rank 3	Regulators
Balanced	A50M60K	Rank 3	Reference
Performance	A30M60K	Rank 1	Operators
Cost	A30M60K	Rank 1	Commercial
Max Decarb	A50M60K	Rank 4	Climate Emergency

Cross-Scenario Robustness Analysis: A30M60K ranking across all five scenarios: Environmental (Rank 3, 0.834), Balanced (Rank 3, 0.832), Performance (Rank 1, 0.843), Cost Minimization (Rank 1, 0.847), Maximum Decarbonization (Rank 4, 0.785). Average ranking: 2.4, achieving top-3 position in 4 out of 5 scenarios (80% robustness).

Pareto Frontier Analysis

Figure 4 presents four critical two-objective Pareto frontier analyses examining trade-offs between competing optimization dimensions:

Figure 4. Four critical Pareto frontier relationships demonstrating A30M60K’s position as efficiency-optimal across multiple objective pairs. Red dashed lines indicate Pareto-efficient frontiers. (Purple/Magenta: A10 (10% ammonia), Cyan: A20 (20% ammonia), Green: A30 (30% ammonia), Light green: A40 (40% ammonia), Yellow: A50 (50% ammonia).

Upper-Left Panel—${CO}_2$ versus Thermal Efficiency: Reveals the carbon reduction cost in efficiency penalties. A30M60K occupies the “knee point” position where maximum thermal efficiency (42%) intersects substantial ${CO}_2$ reduction (55%). The frontier slope at this position (−0.26% ITE per 1% ${CO}_2$ reduction) demonstrates optimal marginal cost compared to −0.50% for configurations beyond 40% ammonia.

Upper-Right Panel—${CO}_2$ versus Power Output: Demonstrates A30M60K’s attractive balance with 6.9 kW output while achieving 55% ${CO}_2$ reduction. The analysis confirms that aggressive carbon reduction beyond 60% necessitates either severe power penalties or advanced combustion strategies.

Lower-Left Panel—${NO}_x$ versus Thermal Efficiency: Illustrates the simultaneous achievability of efficiency improvements and nitrogen oxide reduction in ternary combustion. A30M60K achieves 42% efficiency with 60% ${NO}_x$ reduction, demonstrating synergistic optimization arising from ammonia’s temperature-moderating effect combined with methanol’s oxygen enrichment.

Lower-Right Panel—ISFC versus Power Output: Examines fuel economy versus power delivery capability. A30M60K achieves competitive ISFC (520 g/kWh) while maintaining adequate power output (6.9 kW), representing optimal balance in the moderate-ISFC, moderate-power region of the frontier.

Collectively, these multi-dimensional Pareto analyses quantitatively establish that A30M60K occupies optimal or near-optimal positions across all critical objective pair combinations, providing transparent, reproducible justification for this configuration selection.

2.7. Practical Implementation Considerations

While this CFD study focuses on identifying optimal thermochemical conditions for tri-fuel combustion, practical implementation of the 530 K fuel preheating strategy requires consideration of energy requirements, material compatibility, and safety aspects.

2.7.1. Energy Balance Analysis

Preheating the methanol–diesel fraction (90% of total fuel mass for A30M60K) from ambient temperature (298 K) to 530 K requires approximately 420 kJ/kg fuel energy based on weighted average specific heat capacities (methanol: 2.53 kJ/kg·K, diesel: 1.75 kJ/kg·K). At the simulated fuel consumption rate of 8.5 g/s, this corresponds to a preheating power requirement of approximately 3.57 kW. With the A30M60K configuration producing 6.9 kW indicated power, direct preheating would consume approximately 52% of indicated power output, or approximately 34% of brake power assuming 65% mechanical efficiency. However, practical implementation would recover substantial preheating energy from exhaust waste heat, which represents approximately 40% of fuel energy at exhaust gas temperatures of 650–750 K. A compact exhaust-to-fuel heat exchanger with 60–70% thermal effectiveness (achievable with modern plate-fin or microchannel designs) could recover 2.1–2.5 kW of the required preheating power. This reduces the net parasitic loss to approximately 0.7–1.4 kW, representing 10–13% of brake power output. While this energy penalty is substantial, it may be acceptable considering the A30M60K configuration achieves 58% ${CO}_2$ reduction and 30% thermal efficiency improvement compared to diesel baseline, along with 51% ${NO}_x$ reduction.

2.7.2. Material Compatibility and Safety Considerations

The elevated fuel temperature (530 K = 257 °$\mathrm{C}$) approaches the upper limit of standard automotive fuel system materials but remains within the capability envelope of specialized high-temperature materials. Ammonia–methanol fuel systems at 530 K would require: (1) 316 or 316 L stainless steel fuel lines resistant to ammonia corrosion and methanol solvent effects, (2) perfluoro-elastomer (FFKM) seals rated to 320 °$\mathrm{C}$ rather than standard fluoroelastomer (FKM) seals, and (3) ceramic or plasma-coated injector nozzles to prevent deposit formation and material degradation. Regarding safety, methanol at 257 °$\mathrm{C}$ remains well below its autoignition temperature of 470 °$\mathrm{C}$ and would be maintained in sealed, pressurized fuel systems (3–5 bar) to prevent vapor formation and reduce fire hazard. Fuel preheating would occur immediately before injection in compact heat exchangers integrated into the injection system, minimizing the volume of hot fuel in the system at any time. These design requirements present engineering challenges analogous to those addressed in hydrogen and compressed natural gas (CNG) high-pressure direct injection systems, suggesting technical feasibility.

2.7.3. Alternative Ignition-Assist Strategies

While 530 K preheating emerged as an effective ignition enabler in this computational study, practical implementation may benefit from alternative or complementary ignition-assist approaches that warrant investigation in future work. Potential strategies include: optimized diesel pilot injection timing and quantity to provide localized ignition energy, glow plugs or plasma-assisted ignition systems for thermal/chemical activation, chemical ignition promoters (e.g., di-tertiary butyl peroxide, 2-ethylhexyl nitrate) that could reduce required preheating temperature by 100–150 K, intake air preheating as a less thermally demanding alternative to fuel preheating, and variable compression ratio mechanisms to increase peak compression temperatures. Hybrid strategies combining moderate fuel preheating (e.g., 400–450 K) with pilot injection optimization may provide optimal balance between combustion performance and practical feasibility.

3. Results and Discussion

This section presents a comprehensive analysis of the CFD simulation outcomes, focusing on the impacts of methanol–ammonia–diesel blending ratios and injection temperature on engine performance and emissions characteristics. The validated CFD model was employed to perform detailed parametric investigations. The ammonia energy ratio was systematically varied from 10% to 50% in 10% increments, with methanol energy ratios ranging from 0% to 90% at each ammonia level, while maintaining constant total fuel energy input. At high alcohol concentrations where ignition failed under standard injection conditions (~380 K), preheated injection at 530 K was applied, denoted by (K) in the fuel designation.

3.1. In-Cylinder Pressure

Figure 5 depicts the in-cylinder pressure profiles across varying methanol-ammonia-diesel blend compositions. The results demonstrate that peak cylinder pressure is strongly influenced by both ammonia energy ratio and methanol substitution level, with injection temperature playing a critical modulating role. At 10% ammonia, peak pressures reach 140–145 bar for high-methanol preheated blends, reflecting methanol’s oxygen content that promote enhanced combustion. The underlying chemical mechanism for this improvement lies in methanol’s lower auto-ignition temperature (470 °$\mathrm{C}$) compared to ammonia (657 °$\mathrm{C}$). which serves as a chemical trigger. Methanol oxidation enhances the radical pool, specifically providing a high concentration of $H$ and $OH$ radicals. These reactive species accelerate the thermal decomposition of ${NH}_3$ molecules, effectively compensating for ammonia’s inherently low laminar flame speed (7 $\mathrm{c}\mathrm{m}/\mathrm{s}$ versus 52 $\mathrm{c}\mathrm{m}/\mathrm{s}$ for methanol). When ammonia content increases to 20–30%, preheated blends maintain peak pressures of 130–140 bar, while standard injection exhibits lower pressures (100–120 bar) with retarded combustion phasing.

Figure 5. In-cylinder pressure vs. crank angle for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

At 40–50% ammonia, fundamental combustion limitations emerge. Peak pressures decline to 100–120 bar despite aggressive methanol addition and preheating, reflecting ammonia’s inherently slow flame speed, high auto-ignition temperature (~930 K), and narrow flammability limits. The combustion phasing trends reveal that excessive methanol content under preheating can lead to overly advanced combustion, with peak pressures occurring 5–10° before TDC, resulting in negative compression work.

3.2. In-Cylinder Temperature

Figure 6 presents the mean in-cylinder temperature evolution for the investigated fuel blends. At 10% ammonia, peak temperatures increase from approximately 1800 K (comparable to pure diesel baseline) at low-methanol contents to over 2100 K for high-methanol preheated blends, driven by enhanced combustion completeness and faster premixed kinetics facilitated by methanol’s oxygen content. The introduction of ammonia generally moderates peak temperatures: at 20% ammonia, maximum temperatures reach 1900–2000 K; at 30%, they stabilize around 1800–1900 K; at 40%, they reach 1700–1900 K; and at 50%, maximum temperatures remain below 1800 K. This systematic temperature reduction reflects ammonia’s high latent heat of vaporization (1369 kJ/kg), which consumes substantial thermal energy during evaporation. Also, chemical enthalpy, dilution effects, and combustion efficiency affect the results.

Figure 6. Average in-cylinder temperature vs. crank angle for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

The temperature-moderating effect has important implications for ${NO}_x$ formation, as thermal ${NO}_x$ production follows exponential dependence on temperature. Preheating significantly affects temporal heat release distribution: standard injection blends exhibit broader temperature peaks occurring 5–15° after TDC, while preheated blends display sharper, earlier temperature maxima closer to TDC, reflecting intensified premixed combustion.

3.3. Indicated Specific Fuel Consumption (ISFC)

Figure 7 illustrates indicated specific fuel consumption trends across the blend matrix. At 10% ammonia, ISFC exhibits a U-shaped dependence on methanol content, starting from ~260 g/kWh at diesel baseline and increasing to 380–400 g/kWh at high methanol blends under standard injection. Preheating substantially reduces ISFC to 350–380 g/kWh for M60K–M90K blends, reflecting improved combustion phasing and enhanced fuel-air mixing. At 20–30% ammonia, ISFC increases from diesel baseline (~260 g/kWh) to 360–540 g/kWh with increasing ammonia and methanol content. This increase primarily reflects the lower energy densities of ammonia (18.6 MJ/kg) and methanol (19.9 MJ/kg) compared to diesel (42.5 MJ/kg), requiring substantially greater fuel mass flow rates to maintain constant power output. While preheating improves combustion quality and power output, it cannot overcome the fundamental energy density disadvantage, resulting in higher ISFC values despite enhanced combustion efficiency.

Figure 7. ISFC vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

At 40–50% ammonia, dramatic injection temperature effects emerge. The 40% ammonia M10K configuration shows ISFC of ~640 g/kWh before declining to ~400 g/kWh at M60K, while 50% ammonia achieves optimal ISFC of 430–500 g/kWh at M30K-M60K.

3.4. Indicated Power

Figure 8 presents indicated power output across the blend range. At 10–20% ammonia, power output under standard injection remains at ~6.0 kW, comparable to diesel baseline. However, preheating enables dramatic power increases at high methanol contents, with M80K–M90K blends achieving 9.0–9.5 kW-representing 40–60% improvements over unpreheated operation. At 30% ammonia, power output ranges from 5.0 to 8.5 kW, with peak performance observed around M60K–M70K (~8.0–8.5 kW). The 40% ammonia configuration exhibits non-monotonic behavior: power starts at ~6.0–6.5 kW for STD and M10, then drops to a minimum of ~4.0 kW at M10(K) due to suboptimal combustion phasing with initial preheating application, before recovering to 5.5–8.0 kW for M20(K)–M60(K) blends as methanol content increases.

Figure 8. Indicated power vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

The 50% ammonia series exhibits the most dramatic variations, with M10K producing only 4.2 kW and peaking at ~8.2 kW for M50K. These results demonstrate that achieving diesel-equivalent power with high ammonia substitution requires either moderate ammonia fractions (≤30%) with standard injection or higher fractions (40–50%) with enhanced combustion management. At 40–50% ammonia, maintaining diesel-equivalent power necessitates significant methanol co-fueling (M40K–M60K) combined with fuel preheating at 530 K, making preheating essential for aggressive diesel displacement strategies.

3.5. Indicated Thermal Efficiency (ITE)

Figure 9 displays indicated thermal efficiency, the most comprehensive single-metric assessment of combustion quality. At 10% ammonia, ITE increases from diesel baseline (~31%) to 33–36% with optimal methanol blending, driven by methanol’s oxygen enrichment improving combustion completeness. At 20% ammonia, preheated blends achieve remarkable 35–41% ITE, representing 3–8 percentage point gains through synergistic benefits of advanced ignition timing, faster burn rates, and more complete oxidation.

Figure 9. Indicated thermal efficiency vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

The 30% ammonia configuration demonstrates exceptional efficiency potential, with M50K–M70K achieving 38–42% ITE-exceeding diesel baseline by more than 10 percentage points. At 40% ammonia, efficiency-optimal configurations maintain 35–40% ITE, while 50% ammonia achieves maximum 33–40% ITE with sufficient methanol (50–60%). These results demonstrate that ammonia fractions up to 30–40% can maintain or exceed diesel efficiency with appropriate strategies, though the efficiency-optimal methanol fraction increases approximately linearly with ammonia content.

3.6. Nitrogen Oxides (${NO}_x$) Emissions

Figure 10 presents specific ${NO}_x$ emissions, encompassing both thermal ${NO}_x$ from atmospheric nitrogen and fuel-${NO}_x$ from ammonia’s bound nitrogen. At 10–20% ammonia, ${NO}_x$ ranges from 3 to 9 g/kWh depending on blend composition. Preheating generally reduces ${NO}_x$ by shortening combustion duration and reducing time-integrated exposure to peak temperatures. The 30% ammonia configuration displays remarkable ${NO}_x$ control: M10 and M20 produce only 1.8–2.0 g/kWh under standard injection, while preheated blends M40(K)–M70(K) achieve 2.5–2.8 g/kWh—representing 60–70% reduction versus diesel baseline (~6.6 g/kWh)—through optimized combustion phasing.

Figure 10. ${NO}_x$ emissions vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

At 40–50% ammonia, ${NO}_x$ emissions span 1–8 g/kWh depending on combustion quality. While some configurations achieve 70–85% ${NO}_x$ reductions, these often correspond to poor combustion quality with low power and high ISFC. Efficiency-optimal configurations at 30–40% ammonia achieve moderate 30–60% ${NO}_x$ reductions, demonstrating that substantial greenhouse gas reduction and acceptable nitrogen oxide emissions can be achieved concurrently.

3.7. Carbon Dioxide (${CO}_2$) Emissions

Figure 11 presents specific ${CO}_2$ emissions, the primary greenhouse gas metric. At 10% ammonia, ${CO}_2$ remains relatively stable (550–580 g/kWh) under standard injection compared to diesel baseline (~600 g/kWh), with preheating producing substantial reductions to 270–350 g/kWh at high methanol contents. At 20% ammonia, preheated blends achieve 460–500 g/kWh (14–21% reduction vs. diesel baseline of ~585 g/kWh), combining direct carbon displacement with efficiency improvements. The 30% ammonia configuration produces 240–380 g/kWh (35–55% reduction), with efficiency-optimal M50K–M60K achieving >55% ${CO}_2$ reduction.

Figure 11. ${CO}_2$ emissions vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

At 40% ammonia, M30K–M60K blends deliver 170–230 g/kWh (60–71% reduction), while 50% ammonia achieves maximum 75–78% ${CO}_2$ reduction with efficiency-optimal configurations producing ~130–150 g/kWh. The results reveal a near-linear relationship between ammonia energy fraction and GHG reduction potential, with each 10% ammonia addition providing approximately 10–15% ${CO}_2$ reduction. This linearity is encouraging for decarbonization pathways, suggesting that incremental ammonia adoption can provide proportional emissions benefits.

3.8. Carbon Monoxide ($CO$) Emissions

Figure 12 presents specific $CO$ emissions as an indicator of combustion completeness. At 10% ammonia, $CO$ remains relatively stable at ~110 g/kWh for STD and low methanol blends under standard injection. However, preheating dramatically increases $CO$ emissions, with M20(K)–M90(K) reaching 130–245 g/kWh-representing 20–120% increases versus diesel baseline, due to overly advanced combustion phasing that creates locally rich zones and incomplete oxidation.

Figure 12. Emissions vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

Beyond the influence of locally fuel-rich zones, the thermal properties of the secondary fuels also significantly impact $CO$ formation. Specifically, the high latent heat of vaporization of methanol (1100 kJ/kg, compared to 270 kJ/kg for diesel) induces a pronounced ‘charge cooling effect’ within the cylinder. This localized temperature drop can inhibit the thermal oxidation of $CO$ into ${CO}_2$, as the reaction kinetics for $CO$ oxidation are highly temperature-dependent (requiring temperatures above ~1400–1500 K for the $CO$ + $OH$ → ${CO}_2$ + $H$ reaction to proceed efficiently). Consequently, the cooling effect of methanol evaporation, combined with the oxygen-deprived regions, leads to the higher $CO$ concentrations observed at elevated methanol substitution levels.

At 20–30% ammonia, standard injection achieves the lowest $CO$ emissions (55–90 g/kWh at M10–M20), representing 30–50% reductions versus diesel baseline. In contrast, preheated blends exhibit substantially elevated $CO$ levels (130–215 g/kWh), though values decline progressively with increasing methanol content (M20(K) → M70(K)), indicating improved mixing and oxidation as methanol fraction increases.

Methanol addition specifically mitigates $CO$ and $UHC$ emissions through oxygen enrichment and enhanced reactivity. The bound oxygen content in the methanol molecule (${CH}_{3}OH$) ensures more complete oxidation in locally lean zones where ammonia combustion would otherwise be extinguished. This synergy between methanol’s radical generation and its oxygen-carrying nature promotes the oxidation of intermediate carbon species into ${CO}_2$, even at higher ammonia substitution ratios.

At 40–50% ammonia, $CO$ emissions span 60–170 g/kWh, with declining trends as methanol content increases from M10(K) to M60(K). The results demonstrate that standard injection provides superior $CO$ control at low-moderate ammonia contents (10–30%), while preheating increases $CO$ emissions despite improving ignition and power output, requiring a trade-off between performance and combustion completeness.

3.9. Unburned Hydrocarbons (UHC) Emissions

Figure 13 presents unburned hydrocarbon emissions. Preheating dramatically reduces UHC at high methanol contents, with M60K–M90K achieving near-zero UHC (<5 g/kWh), representing 80–95% reductions.

Figure 13. UHC emissions vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

At 40–50% ammonia, UHC ranges from 4 to 65 g/kWh, with general declining trends as methanol content increases. The results reveal U-shaped relationships with methanol content, minimizing at moderate substitution levels (M40K–M60K) where the balance between charge preparation, ignition energy, and combustion phasing is optimized. Optimal UHC control requires careful matching of fuel composition, injection temperature, and combustion timing.

3.10. Unburned Ammonia (${NH}_3$) Emissions

Figure 14 presents unburned ammonia emissions, a critical environmental concern. The study results reveal that combustion temperature and alcohol ratio significantly influence unburned ammonia (${NH}_3$) emissions. At 10% ammonia, unburned ${NH}_3$ ranges from 2 to 19 g/kWh, with preheating providing 30–60% reductions through advanced combustion phasing.

Figure 14. ${NH}_3$ emissions vs. fuel blend for ammonia ratios of 10%, 20%, 30%, 40%, and 50%.

The 50% ammonia configuration demonstrates severe slip (23–130 g/kWh), with even best-case scenarios representing 20–25% of the initial ammonia charge escaping unburned. These results indicate that while ${CO}_2$ reductions of 50–78% are achievable at high ammonia fractions, unburned ${NH}_3$ increases by an order of magnitude as ammonia content rises above 30–40%.

The substantial increase in unburned ammonia emissions, particularly at AER levels exceeding 40%, highlights a critical challenge for the practical implementation of ammonia-fueled engines. For these configurations to be commercially viable and environmentally compliant, the integration of advanced after-treatment systems is essential. Specifically, an Ammonia Slip Catalyst (ASC) or an optimized Selective Catalytic Reduction (SCR) system would be required to oxidize the escaped ${NH}_3$ into $N_2$ and $H_{2}O.$ Modern ASC systems are capable of achieving high conversion efficiencies, effectively bridging the gap between engine-out slip levels and stringent emission limits. The implementation of such catalysts would effectively bridge the gap between high engine-out slip levels and stringent emission requirements.

3.11. Stakeholder-Specific Configuration Recommendations

Multi-objective optimization analysis reveals that configuration optimality depends critically on operational priorities and stakeholder value systems. Based on five scenario analysis spanning diverse weighting schemes, we provide explicit recommendations tailored to different operational contexts:

For Balanced Multi-Objective Performance: Configuration: A30M60K (30% ammonia, 60% methanol, 530 K preheating) Rationale: Achieves consistent top-3 ranking across 80% of scenarios (4 out of 5), with rank-1 position in both performance-focused and cost-minimization scenarios. Delivers peak thermal efficiency (42%), substantial emissions reduction (58% ${CO}_2$, 51% ${NO}_x$), enhanced power output (+11%), and minimum fuel consumption. Application: General-purpose marine propulsion, multi-mission vessels, operators prioritizing operational flexibility and long-term cost-effectiveness.

For Maximum Environmental Impact: Configuration: A50M60K (50% ammonia, 60% methanol, 530 K preheating) Rationale: Achieves maximum ${CO}_2$ reduction (78%) with acceptable efficiency penalty (34% ITE, −8 percentage points versus A30M60K). Optimal for scenarios where carbon reduction supersedes all other considerations. Application: Zero-emission port operations, climate emergency mandates, regulatory environments requiring > 70% carbon reduction, operators with carbon pricing pressure.

For Commercial Cost Optimization: Configuration: A30M60K or A30M50K (30% ammonia, 50–60% methanol) Rationale: Minimizes fuel consumption (520 g/kWh for A30M60K), maximizes thermal efficiency, requires moderate engine modifications. Optimal operating cost structure in competitive freight markets where fuel cost dominates (70–80% of operating expense). Application: Commercial shipping, cost-sensitive operations, freight transport, operators prioritizing economic viability over maximum decarbonization.

For Performance-Critical Applications: Configuration: A30M60K (or A50M60K (maximum power) Rationale: A30M60K achieves peak thermal efficiency with 11% power increase versus baseline; A50M60K provides maximum power output (8.2 kW, +32% versus baseline) when power delivery supersedes efficiency considerations. Application: High-performance vessels, applications requiring maximum power density, operations with frequent high-load demands.

For Moderate Decarbonization Targets (50–60% ${CO}_2$ reduction): Configuration: A30M60K Rationale: Achieves 58% ${CO}_2$ reduction with peak thermal efficiency and maintained power output. Represents optimal balance point for moderate emission reduction targets without severe performance penalties. Application: Compliance with intermediate emission standards operators balancing environmental and performance objectives.

For Aggressive Decarbonization Targets (65–70% ${CO}_2$ reduction): Configuration: A40M60K (40% ammonia, 60% methanol, 530 K preheating) Rationale: Achieves 71% ${CO}_2$ reduction with 2.5 percentage point efficiency penalty versus A30M60K. Represents compromise solution for stringent but not extreme emission requirements. Application: Strict regulatory environments, early-adopter operators, demonstration projects for advanced alternative fuel systems.

Configuration Selection Decision Framework: The optimal configuration depends on the relative prioritization of environmental compliance requirements (regulatory mandates, voluntary commitments), economic constraints (fuel cost sensitivity, capital investment availability), operational requirements (power output demands, efficiency targets), and technological readiness (engine modification complexity, fuel infrastructure).

For most practical applications, A30M60K emerges as the recommended configuration due to its exceptional cross-scenario robustness, balanced multi-objective performance, and feasible implementation pathway. Alternative configurations (A40M60K, A50M60K) should be considered only when specific operational contexts demand higher carbon reduction despite associated efficiency penalties and practical challenges.

4. Conclusions

This computational investigation systematically examined diesel–methanol–ammonia combustion in a compression-ignition engine across a comprehensive parameter space encompassing ammonia energy ratios from 10% to 50% and methanol substitution levels from 0% to 90%. Using a validated CFD model on the single-cylinder engine (deviation < 5% versus experimental data), this study quantified the complex interactions between fuel composition, combustion characteristics, thermal efficiency, and emission formation across 50+ operating configurations. The investigation reveals that strategically optimized ternary-fuel operation can achieve substantial carbon dioxide reductions while maintaining acceptable engine performance, though fundamental limitations emerge at high ammonia substitution ratios.

The study establishes that ternary-fuel combustion maintains diesel-comparable performance at ammonia energy ratios up to 30–40% when appropriate methanol substitution (M30K–M60K) and fuel preheating (530 K) are employed. Peak cylinder pressures of 130–145 bar and temperatures of 1900–2100 K are achievable, demonstrating that moderate ammonia fractions can be integrated without substantial hardware modifications. Beyond 40% ammonia, systematic combustion degradation occurs-peak pressures decline to 100–120 bar and temperatures to 1600–1800 K-reflecting ammonia’s fundamental limitations (flame speed 7 $\mathrm{c}\mathrm{m}/\mathrm{s}$, auto-ignition temperature 657 °$\mathrm{C}$) that methanol’s combustion-enhancing properties cannot fully overcome.

Thermal efficiency analysis reveals an optimal operating window at 30% ammonia with M50K–M60K methanol substitution, achieving 38–40% indicated efficiency-representing 5–8 percentage point improvements over baseline diesel operation. This enhancement stems from improved combustion phasing, reduced heat transfer losses, and more complete oxidation facilitated by methanol’s bound oxygen. Each 10% ammonia increment beyond 30% incurs approximately 2–3 percentage points efficiency penalty, with maximum efficiency declining to 30–32% at 50% ammonia despite aggressive optimization.

Emission characteristics demonstrate that ${CO}_2$ reductions scale approximately linearly with ammonia energy fraction: 14–21% at 20% ammonia, 35–55% at 30%, 60–71% at 40%, and 75–78% at 50%. The optimal configuration (30% ammonia + 60% methanol with preheating) delivers simultaneous 55% ${CO}_2$ reduction, 60% ${NO}_x$ reduction, and 38% thermal efficiency. However, $CO$ emissions increase 20–60% due to locally rich combustion zones, while unburned hydrocarbons minimize at M40K–M60K. The comprehensive emission mapping across the parameter space provides quantitative trade-off data essential for balancing environmental and performance objectives.

Five-scenario multi-objective optimization analysis demonstrates that A30M60K (30% ammonia + 60% methanol + 10% diesel with 530 K preheating) emerges as the optimal tri-fuel configuration for balanced multi-objective performance, demonstrating exceptional cross-scenario robustness (top-3 ranking in 4 out of 5 stakeholder scenarios). This configuration achieves 42% indicated thermal efficiency, 58% ${CO}_2$ reduction, 51% ${NO}_x$ reduction, and 11% enhanced power output versus diesel baseline. A30M60K occupies the efficiency-optimal “knee point” on the Pareto frontier where maximum thermal efficiency intersects substantial carbon reduction. The superiority stems from optimal combustion phasing (CA50 at 8–10° ATDC), peak in-cylinder temperatures of 2050–2100 K, and balanced chemical kinetics where 60% methanol compensates for ammonia’s low reactivity. Configurations beyond 30% ammonia exhibit diminishing returns, with marginal ${CO}_2$ reduction benefits declining from 12.2% per efficiency point at 30% ammonia to only 5.9% at 50% ammonia. Configuration optimality depends on stakeholder priorities: A30M60K optimal for balanced performance and cost minimization, while A50M60K preferred for maximum decarbonization scenarios (78% ${CO}_2$ reduction with 8 percentage point efficiency penalty). Configuration A40M60K may be considered when regulations mandate 65–70% carbon reductions, accepting 2.5 percentage point efficiency penalty. For practical implementation, A30M60K requires moderate engine modifications and offers feasible pathway to 50–60% ${CO}_2$ reduction. Standard injection (380 K) should be limited to ammonia below 30%, with preheating to 530 K essential for higher substitution levels to achieve stable combustion and reduce unburned ammonia from 180 ppm to 45 ppm.

The 530 K fuel preheating strategy identified as enabling stable high-alcohol combustion represents a computational finding requiring practical implementation development. While energy balance analysis indicates that exhaust waste heat recovery could reduce net parasitic power loss to 10–13% of brake output-potentially acceptable given the 58% ${CO}_2$ reduction and 30% efficiency improvement-practical deployment requires addressing material compatibility (specialized high-temperature fuel system materials), safety considerations (pressurized sealed systems), and potentially exploring alternative or complementary ignition-assist methods including optimized pilot injection strategies, glow plug or plasma ignition systems, chemical ignition promoters, intake air preheating, or variable compression ratio mechanisms. Future experimental investigations should systematically evaluate these alternatives and hybrid approaches to establish a comprehensive ignition management framework balancing combustion performance with practical feasibility and economic viability.

Finally, these findings establish a theoretical foundation for next-generation injection system development. Based on the energy analysis presented in Section 2.7, we envision practical implementation through an integrated injector assembly incorporating localized thermal assist-such as an injector-integrated glow plug or micro-heater element. Future experimental studies should validate this concept and evaluate alternative ignition-assist strategies including chemical promoters and plasma-assisted ignition.