The choice of fuel type is a foundational variable in engine design and performance optimization, particularly within the controlled environment of simulation models. These digital testbeds allow engineers to explore the effects of fuel properties—such as energy density, octane or cetane rating, volatility, and oxygen content—without the cost and time of physical prototyping. By systematically varying fuel inputs, simulation models reveal how combustion characteristics, thermal efficiency, power output, and emissions interact. This expanded article delves deeper into the mechanics, methodologies, and real-world implications of fuel type selection in engine simulation, providing a comprehensive resource for engineers and fleet managers alike.

Fundamentals of Fuel Properties in Simulation

Simulation models rely on accurate fuel property data to predict engine behavior. Key properties include:

  • Energy content (Lower Heating Value, LHV): Determines the maximum energy available per unit mass or volume. Gasoline has an LHV of ~44 MJ/kg, diesel ~45.5 MJ/kg, and ethanol ~26.8 MJ/kg. Electric motors measure energy in kWh, but the equivalent fuel energy must account for generation and transmission losses.
  • Octane number (for spark-ignition engines): Indicates resistance to knock. Higher octane allows higher compression ratios and advanced spark timing, improving thermal efficiency but raising fuel cost.
  • Cetane number (for compression-ignition engines): Measures ignition delay. Higher cetane diesel fuels promote smoother combustion and lower noise, but may reduce low-power fuel economy.
  • Volatility and vapor pressure: Affects mixture formation, cold-start performance, and evaporative emissions. Simulation models must account for fuel evaporation rates in intake manifold or direct injection systems.
  • Oxygen content: Fuels like ethanol and biodiesel contain oxygen, which affects stoichiometry, combustion temperature, and NOx formation.
  • Viscosity and lubricity: Critical for fuel injection systems, especially in diesel engines. Low-viscosity fuels can cause leakage and wear in high-pressure pumps.

These properties are inputs to both thermodynamic zero-dimensional models (e.g., Wiebe function based) and three-dimensional computational fluid dynamics (CFD) simulations that resolve flame propagation, turbulence, and pollutant formation.

Simulation Methodologies for Fuel Effects

Zero-Dimensional and Quasi-Dimensional Models

These models use simplified heat release profiles and empirical correlations for emission formation. They are computationally efficient and suitable for parametric studies across a broad range of fuel types. For example, a zero-dimensional model can quickly simulate how switching from E10 gasoline to E85 anhydrous ethanol reduces peak cylinder temperature and lowers NOx output while requiring more fuel mass to maintain power.

Computational Fluid Dynamics (CFD) Simulations

CFD tools like Converge, Star-CD, and OpenFOAM simulate the in-cylinder processes in high detail: spray breakup, wall impingement, evaporation, turbulent mixing, chemical kinetics, and soot oxidation. Fuel properties influence the breakup length and droplet size distribution, which in turn affect the air-fuel mixture homogeneity. A study by scientists at Argonne National Laboratory highlighted that higher viscosity fuels produce coarser sprays, leading to incomplete combustion and increased particulate matter. CFD simulations also incorporate reduced chemical mechanisms (e.g., for gasoline surrogate or diesel surrogate reaction mechanisms) to predict intermediate species like CO, H₂, and aldehydes.

Machine Learning-Enhanced Surrogate Modeling

Recent advances use machine learning (ML) to build surrogate models that map fuel properties directly to performance and emissions. These models are trained on high-fidelity CFD runs or experimental data. For instance, a neural network can predict the bsfc (brake specific fuel consumption) and NOx trade-off for a given fuel composition, enabling rapid fuel screening. However, ML models require careful validation to avoid extrapolation errors, especially for unconventional fuel blends.

Detailed Impact by Fuel Type

Gasoline (Conventional and Blends)

Gasoline engines operate on the Otto cycle with spark ignition. Simulation models consistently show that higher octane rating allows engineers to increase the geometric compression ratio—from around 9:1 for 87 RON to 11:1 or even 13:1 for 98 RON—yielding a 3–5% improvement in thermal efficiency. However, the benefit saturates once knock is fully suppressed, and higher octane fuels cost more. Blends like E10 (10% ethanol) and E85 (up to 85% ethanol) introduce oxygen, which leans the mixture slightly. CFD simulations reveal that E85’s high latent heat of vaporization cools the intake charge, reducing knock in turbocharged engines and allowing more aggressive spark timing. The downside is a 30% increase in volumetric fuel consumption due to lower energy density. EPA data on gasoline blends provides a benchmark for such simulations.

Diesel and Biodiesel Blends

Diesel engines (compression ignition) rely on auto-ignition. Simulation models emphasize the cetane number's effect on ignition delay. Low-cetane fuels cause longer ignition delay, more premixed burn, higher peak pressure rise rates, and increased noise and NOx. High-cetane fuels shorten delay, reducing peak temperature but potentially increasing soot formation due to less premixed combustion. Biodiesel (B20, B100) has higher viscosity and density, causing spray penetration to increase. CFD results show that biodiesel droplets travel further before vaporising, leading to more wall wetting and increased unburned hydrocarbon (UHC) emissions. However, biodiesel fuels are oxygenated (10–12% oxygen), which reduces soot at high loads. A comprehensive NREL study on biodiesel combustion documents these trade-offs. In simulations, engineers often adjust injection timing to minimise the NOx-soot trade-off when switching from petroleum diesel to biodiesel.

Alternative Fuels: Hydrogen, Ammonia, and Methanol

Hydrogen

Hydrogen has the highest mass energy density (120 MJ/kg) but very low volumetric density (0.0899 g/L at STP). In simulation, hydrogen combustion is modeled with detailed chemical kinetics (over 30 reactions). Benefits include ultra-lean burn capability (phi as low as 0.2), near-zero carbon emissions, and high flame speed. Challenges include pre-ignition, backfiring, and NOx formation at high load. Injection strategies such as direct injection of cryogenic hydrogen can mitigate knock. SAE technical paper 2019-24-0081 explores hydrogen engine simulations in detail.

Ammonia

Ammonia (NH₃) is a carbon-free fuel with a high hydrogen density. Its low flame speed and high auto-ignition temperature make it challenging to burn in conventional spark-ignition engines. Simulation models show that ammonia needs a pilot fuel (e.g., diesel or hydrogen) to sustain combustion. CFD analysis highlights its high latent heat, which reduces compression temperatures, and its tendency to produce NOx emissions through fuel-bound nitrogen. Nonetheless, ammonia is gaining attention for marine applications where carbon abatement is critical.

Methanol

Methanol has a high octane number (107 RON) and low combustion temperature, reducing NOx significantly. Simulation models reveal that methanol’s higher heat of vaporisation enables efficient charge cooling, allowing high compression ratios and boost pressures. However, its energy density is half of gasoline, and its corrosive nature requires material compatibility considerations. Optimising injection timing and spark delay in methanol engines can achieve thermal efficiency above 40% in simulation.

Electric Propulsion in a Fuel Context

While not a “fuel” in the combustion sense, electric powertrains are often compared in simulation models alongside fuels. The key performance “fuel” parameter is battery specific energy (currently ~250 Wh/kg for Li-ion). Simulation models compare “well-to-wheel” efficiency: electric motors convert >90% of stored energy to motion, whereas ICEs achieve 30–40%. However, the source of electricity must be accounted. Models like Argonne’s Autonomie simulate full vehicle energy flow, including battery thermal management and regenerative braking. The DOE Vehicle Technologies Office provides tools for such comparisons.

Simulation Observations: Performance Metrics by Fuel

Below is a consolidated summary of typical simulation outputs for different fuel types under similar engine configurations (e.g., 2.0L turbocharged, 10:1 CR for gasoline, 16:1 for diesel, 100 kW target power). Values are representative and depend on specific model calibration.

Fuel Type Brake Thermal Efficiency (%) Peak Power (kW/L) NOx (g/kWh) PM (g/kWh) CO2 (g/kWh, tank-to-wheel)
Gasoline (E10, RON 95)36–3980–900.8–1.20.01260–280
Diesel (B7)42–4570–802.5–4.00.05230–250
E8538–4285–950.5–0.90.02210–230*
Biodiesel (B100)40–4365–752.0–3.50.03150–170*
Hydrogen (direct injection)42–4850–601.5–3.000
Electric (grid mix)70–85 (well-to-wheel)120–150 (motor)00100–200*

*CO2 includes upstream production for biofuels/electricity. Hydrogen CO2 depends on production method (grey, blue, green).

These simulation results underscore that no single fuel is optimal across all metrics. Engine designers must prioritise based on application: heavy-duty long-haul may favour diesel’s high efficiency, while urban light-duty vehicles benefit from electric’s zero tailpipe emissions. Alternative fuels like hydrogen and methanol offer promising pathways for decarbonisation but require significant engine modifications and infrastructure.

Practical Implications for Fleet Operators

Fuel Selection and Engine Calibration

Simulation models allow fleet engineers to pre-validate fuel changes before committing to fleet-wide adoption. For example, switching from conventional diesel to a B20 blend typically increases NOx by 2–5% but reduces PM. By simulating the engine control unit (ECU) parameters—such as exhaust gas recirculation (EGR) rate, injection timing, and boost pressure—engineers can recalibrate to recover the NOx advantage. Similarly, a fleet considering hydrogen retrofit must simulate the risk of pre-ignition under high load and adjust injection window accordingly.

Maintenance and Durability

Fuel properties affect wear, deposit formation, and lubrication. Simulation models that include component fatigue and corrosion can predict reduced injector life for low-viscosity fuels or fuel with high sulfur content. Fleet managers can use these simulations to plan maintenance intervals and avoid costly unscheduled downtime.

Regulatory Compliance

Emissions regulations (EPA Tier 4, Euro VI, CARB) require compliance over the vehicle’s lifetime. Simulation models incorporating fuel effects help ensure that a fleet’s chosen fuel and calibration will stay within limits under real-world driving cycles. For example, the transition to ultra-low sulfur diesel (ULSD) was modeled extensively to verify that aftertreatment systems would not be poisoned by sulfur. EPA diesel fuel standards provide the regulatory framework.

The push toward net-zero emissions is driving two parallel research avenues: carbon-neutral synthetic fuels (e-fuels) and advanced battery electric as well as fuel cell vehicles. Simulation models are being extended to handle renewable fuels such as electro-methanol, Fischer-Tropsch diesel from CO₂, and ammonia. Multi-component fuel surrogates capable of representing real fuel blends with dozens of chemical species are entering mainstream CFD. At the same time, machine learning is enabling real-time near-optimal calibration across a range of fuels, reducing the need for extensive dynamometer testing.

One exciting development is the “digital twin” engine that continuously updates its model using sensor data from the physical engine. This system can adjust fuel injection parameters on-the-fly as fuel properties vary from batch to batch, maximising efficiency and minimising emissions. Fleet operators in the near future may rely on such adaptive control to seamlessly switch between diesel, biodiesel, or e-fuels without hardware changes.

Conclusion

Simulation models have become indispensable tools for understanding the intricate relationship between fuel types and engine performance. They allow engineers to dissect the effects of fundamental fuel properties—from energy content to volatility to chemical composition—on metrics that matter: power, efficiency, emissions, and durability. Gasoline and diesel remain benchmarks, but alcohols, biofuels, hydrogen, and ammonia each present unique performance and environmental profiles. The choice is never simple; it involves trade-offs that simulation can quantify. As regulatory pressure and sustainability goals evolve, simulation tools will continue to steer the development of cleaner, more efficient engines and help fleet operators make data-driven fuel decisions. The future is not about a single best fuel but about a flexible, adaptive system capable of leveraging the strengths of many.