Effect of Variable Compression Ratio on the Thermal Characteristics of Diesel Engine Powered by Waste Tires Blends

Several factors, including the shortage of fossil fuels, rising oil prices, and environmental concerns, have driven extensive research into sustainable alternative fuels. Waste tire pyrolysis oil (WTO) has been considered a possible alternative to diesel fuel in compression-ignition engines. The management of waste tires has become an increasingly serious issue, with waste tire generation worldwide estimated at 1.5 billion pieces each year [1, 2]. As a result of their recalcitrance to decomposition, they accumulate as waste in landfills or are incinerated, leading to contamination of soil, air, and water [3, 4]. Pyrolysis has been a sustainable, thermally efficient approach for converting waste tires into pyrolysis oil, gas, and char. Among these, WTO is an alternative or supplemental fuel for diesel engines owing to its high calorific value (up to 45.6 MJ/kg) [5]. Tire pyrolysis oil, a type of tire oil, is derived from the pyrolysis of waste tires to generate fuel. Besides being high in energy and extraordinarily cheap, oil is consistently high in viscosity, so even the crude diesel engine immediately got a terrible rating for blowing pollutants out the tailpipe. As a result, attempts have been made to blend the WTO with diesel fuel to improve engine efficiency and reduce emissions, without affecting existing engine technologies [6, 7]. WTO has a higher density (up to 923 kg/m³), viscosity (up to 4.35 mm²/s), and a lower cetane number (CN) (as low as 42–48), although its high value of heating contrasted to traditional diesel and is required to be used in admixed form.

The behavior of the WTO in compression-ignition engines under various operating conditions has been investigated in several studies. For instance, Kapilan et al. [8] conducted a comparative experimental analysis of Butylated Hydroxy Anisole (BHA), an antioxidant, in dewatering waste tire oil-diesel combinations in a compression-ignition engine. They reported that a BHA content of 1000 ppm in D80WTO20 increased brake thermal efficiency (BTE) (32.92%) and reduced emissions (NO_x: 1406 ppm, CO: 0.224%). Karagöz [9] presented a laboratory inquiry to assess the performance of a diesel engine operated on waste tire oil (WTO)-dexlite combinations. The engine operated over 1500–2100 rpm with WTO mixes at 10, 20, and 30%. The engine achieved a maximum output power of 2.32 kW with a specific fuel consumption of 0.48 kg/kWh. Additionally, Kondor et al. [10] conducted laboratory tests on a VCR diesel engine operated with blends of WTO and diesel. The performance and emissions of WTO blends at 10%, 25%, 50%, and 75% were optimized in the investigation. The results showed that BTE increased and fuel consumption decreased at high compression ratios (CRs). Furthermore, Kumaravel et al. [11] investigated the effectiveness of tire-derived fuel (TDF) in diesel blends at 10%, 30%, and 50%. The performance results revealed that TDF10% was the best source for power production, achieving a maximum increase of 4.9% over diesel. Furthermore, Öner et al. [12] investigated the combustion behavior, performance, and emissions of an engine using blends of crude pyrolytic oil (CPO) with diesel and butanol in an experimental study. Preliminary tests on seven blends showed good engine performance, and the peak brake-specific fuel consumption (BSFC) was 425.35 g/kWh for PO30Bu20CN1. Another study was conducted by other researchers on an experimental basis, using diesel blends containing 10% waste plastic oil (P10) and 10% waste tire oil (T10) in diesel engines. The findings showed that the BSFC of P10 was 0.33 kg/kWh at 100% load, and the value of BSFC for T10 was 0.35 kg/kWh. The highest cylinder pressure at full load was 71.39 bar for P10 and 72.01 bar for T10 [13]. In addition, Mohiuddin et al. [14] conducted laboratory analysis of a dual-fuel engine operating on a mixture of waste tire-derived pyrolysis oil and biogas. They investigated five fuel blends with different biogas flow rates, reporting improvements in BSFC and reductions in BTE; NOx emissions were reduced by up to 11.14% with biogas addition. Also, Nabi et al. [15] conducted an experimental investigation of the combustion behavior of crude tire fuel blends in a diesel engine. They used 5% and 10% tire fuel blends with diesel, and the results showed that peak cylinder pressure decreased by 6.69% and net heat release increased by 19.33%. Similarly, Öner et al. [12] examined the effects of tire pyrolysis oil blends on diesel engine performance. They experimented with WTO10 and WTO20 and observed a 4% decrease in BTE and a 15.3% increase in CO emissions at full load. Isah et al. [7] experimentally examined the engine output (performance) and emissions of waste tire oil, diesel, and Glycine max biodiesel blends. They experimented with different mixtures and found that tire oil at up to 30% performed comparably to diesel, with only a marginal increase in emissions and fuel consumption. In addition, Ghareeb and Anjal [16] experimented with aromatic-enriched WTO combustion performance. Burning rate for SRTO at 25 ℃ was 1.93 [3180.03 mm²/s, and the apparent activation energy for SRTO was 37.2 KJ/mol. Pote and Patil [17] conducted a trial analysis of a diesel engine using blends of WTO, diesel, and hydrogen. At 2000 rpm, the W15 blend produced 184 Nm of torque, 62 kW of power, and 332 g/kWh of BSFC. Adding hydrogen to W15H produced a higher performance of 189.2 Nm, 64 kW of power, and 327 g/kWh BSFC, with lower CO, CO₂, and NO_x emissions. Moreover, Pote and Patil [18] conducted an experimental study on a Mitsubishi S4S-DT engine operating on a blend of WTO and diesel at 2.5%, 5%, 7.5%, and 10% proportions. Results indicate a marginal power decrement and a 10% increase in BSFC at 40% load, and CO₂ emissions are marginally lower by 1% for the WTO10 blend. Besides that, Dasari et al. [4] reported research on the performance and emissions characteristics of the engine using pyrolysis fuel from waste plastics blended with RON 90 gasoline. Testing revealed that 1.05 hp was the maximum power at 4000 rpm using the PE-RON 90:20:80 mixture, with minimal CO emissions (0.78% at 6000 rpm). In a similar line, Saravanan et al. [19] conducted an experimental investigation on the effect of BHA antioxidants on waste tire oil-diesel blends as fuel in a compression-ignition engine. At 1000 ppm BHA in D80WTO20, the BTE improved to 32.92%, and emissions went down to 1406 ppm of NO_x and 0.224% of CO. Doğan et al. [20] investigated the combustion performance of crude TDF blended with diesel in a single-cylinder diesel engine. Higher peak cylinder pressure was observed by 6.69% for a 10% TDF blend and by 19.33% greater gross heat emission than diesel, while ignition delays were almost the same. Furthermore, Kaimal et al. [21] conducted experiments to analyze the combustion characteristics, performance, and emissions of a diesel engine that used a blend of 10% WTO and diesel fuel. Results demonstrated that W 10 enhanced in-cylinder pressure (71.56 bar at full load) and heat release rate, while resulting in a 10.9% decrease in soot and a 13.7% decrease in CO.

Previous investigations of diesel engines fueled with tyre pyrolysis oil (WTO) consistently indicate an increase in BSFC and a deterioration in BTE with increasing WTO content, primarily due to its low cetane number, high aromatic content, and inferior atomization behavior. Although reductions in NO_x emissions are frequently reported, these benefits are often counterbalanced by increased smoke emissions, while combustion characteristics remain highly inconsistent across studies due to variations in engine configuration and operating conditions. More critically, the majority of experimental and numerical studies assess either the WTO blend ratio or engine parameters, most notably the CR, under fixed or isolated conditions, with some relying on dual-fuel enhancement strategies to offset performance penalties. This fragmented research approach fails to capture the coupled, interactive effects of fuel properties and engine calibration, thereby limiting the development of robust, transferable, and practically applicable optimization strategies for WTO-fueled compression-ignition engines.

To address these limitations, the present study introduces a validated numerical framework that systematically integrates WTO blend ratio analysis (10% and 20%) with CR optimization over a wide range (15:1–19:1). The novelty of this work lies in its unified and holistic approach, which simultaneously evaluates fuel chemistry effects, engine parameter tuning, and performance–combustion–emissions trade-offs within a single modeling environment. The study is designed to quantify the influence of WTO blends on engine performance, combustion behavior, and emissions relative to conventional diesel fuel, while elucidating the role of CR variation in improving WTO combustion efficiency and stability. By establishing application-oriented optimization strategies that balance efficiency gains with emission constraints, this work provides actionable insights to advance the practical calibration and deployment of WTO as a sustainable alternative fuel in compression-ignition engines.

The spray, combustion, NO_x formation, and soot models employed in Diesel-RK are based on well-established and widely validated formulations reported in the literature. In the present study, the influence of WTO is incorporated through its specific physicochemical properties, including cetane number, viscosity, elemental composition, molecular mass, and LHV. These properties directly influence ignition delay, spray atomization, evaporation behavior, heat release phasing, and emission formation, allowing the distinct combustion characteristics of WTO to be accurately represented without altering the fundamental structure of the governing models [24].

The Diesel-RK software's fuel-property-driven approach uses input characteristics (cetane number, viscosity, density, LHV, elemental composition) to capture fuel-specific combustion behavior. This enables accurate WTO simulation without model restructuring, as properties directly influence spray behavior, ignition timing, heat release, and emissions.

3.1 Spray assessment model

The fuel journey from the injector nozzle into the combustion chamber is critical, as it determines the characteristic (quality) of the air-fuel mixture. The spray model tracks discrete parcels of fuel, known as Elementary Fuel Masses (EFMs), using velocity-penetration equations [25].

WTO's higher viscosity (2.47 mm²/s vs. 2.24 mm²/s for diesel) results in poorer atomization, larger droplets, and altered spray development, captured through viscosity inputs without equation modifications.

Figure 1. Visual figure of the fuel spray

Eq. (1) describes the velocity of an EFM transmitted from the injector to the spray head during a discrete time step, as illustrated in Figure 1.

$\left[\frac{U}{U_0}\right]^{\frac{3}{2}}=1-\frac{l}{l_m}$      (1)

where, $U$ denotes the instantaneous velocity of the EFM, $U_0$ Represents its initial velocity at the injector nozzle, $l$ is the instantaneous distance between the EFM and the injector nozzle, and $l_m$ Corresponds to the maximum penetration length for the EFM at the spray front. The differential Eq. (1) is then in part solved as follows:

$3 l_m\left[1-\left[1-\frac{l}{l_m}\right]^{0.333}\right]-U_o \tau_k=0$       (2)

where, $\tau_k$ denotes the travel time of the EFM to cover the distance $l$ from the injector nozzle; when the EFM attains the spray tip and comes to rest at $l=l_m$. The corresponding travel time is $\tau_k=\tau_m$.

where, $\tau_m$ - duration period for the EFM to reach the spray’s front before finishing.

Eq. (2) can be written as:

$l_{\mathrm{m}}=\mathrm{U}_{\mathrm{o}} \frac{\tau_{\mathrm{m}}}{3}$      (3)

From Eqs. (1)–(3), The instantaneous velocity and EFM length of penetration are determined as follows:

$U=U_o\left[1-\frac{\tau_k}{\tau_m}\right]^2$       (4)

$l=l_m\left[1-\left[1-\frac{\tau_k}{\tau_m}\right]^3\right]$    (5)

Therefore, solving Eq. (1) yields the travel time and dynamic position of each fuel parcel, Eqs. (2)–(5), directly influencing evaporation and mixing rates. This is crucial for simulating WTO blends, as their higher viscosity would result in poorer atomization and larger droplet sizes, affecting the subsequent $(l)$ and $\left(l_m\right)$ values.

3.2 Heat release model

The combustion process is not monolithic but a sequence of distinct phases governed by different physical and chemical principles.

The model captures this by dividing combustion into four stages, as illustrated below [25]:

1. The ignition lag period phase can be collected from:

$\begin{gathered}\tau=\sqrt{\frac{T}{P}} * e^{\left(\frac{E_a}{8.312 T}-\frac{70}{C N+25}\right)} * 3.8 * 10^{-6} *(n * 1 \left.-1.6 * 10^{-4}\right)\end{gathered}$    (6)

The last ignition lag equation incorporates cetane number directly - WTO's lower CN (52.06 vs. 53.4) mathematically increases the delay period, inherently capturing altered ignition chemistry.

This phase is governed by pre-flame chemical kinetics, heavily influenced by the fuel's cetane number and the in-cylinder temperature and pressure at injection. A lower CN, such as that of WTO, directly increases this delay.

2. Premixed Combustion Phase: This stage refers to the combustion of the air–fuel vapor mixture formed before ignition.

$\begin{aligned} \frac{d x}{d t}=\varphi_1\left(\frac{d \sigma_u}{d \tau}\right)+ & \varphi_o *\left[\left(\sigma_{u d}-x_o\right)\left(0.1 \sigma_{u d}+x_o\right) A_o\left(\frac{m_f}{V_i}\right)\right]\end{aligned}$   (7)

This rapid, intense burning phase consumes the fuel vaporized and mixed with air within the ignition lag period. A longer delay typically leads to a larger, more vigorous premixed phase.

3. Diffusive Combustion Phase: This stage corresponds to the direct combustion of fuel as it is injected into the chamber.

$\frac{d x}{d \tau}=\varphi_2\left(\left(\sigma_u-x\right)(\emptyset-x) * A_2\left(\frac{m_f}{V_c}\right)\right)+\varphi_1\left(\frac{d \sigma_u}{d \tau}\right)$       (8)

This phase is controlled by the rate at which fuel and air mix. It is highly dependent on spray characteristics and in-cylinder turbulence. Fuels with poorer atomization (higher viscosity) tend to have a more prolonged diffusive burn.

4. Late Burning Phase: This stage refers to the combustion of residual fuel after injection is complete.

$\frac{d x}{d \tau}=(1-x)\left(\varepsilon_b \emptyset-x\right) * \varphi_3 K_T A_3$   (9)

$\varphi_3=\varphi_2=\varphi_1=\varphi_o$, it is a function that represents the completion of the combustion of fuel vapor in regions:

$\phi=1-\left(A_1 / \varepsilon_b \varnothing-x\right) \frac{d x}{d t}\left\{r_v+\sum_{i=1}^{m_w}\left[r_{w i} * 300 * e^{\left(\frac{-16000}{2500+r_{w i}}\right)}\right]\right\}$      (10)

where, $\varepsilon_b$ indicates the efficiency of air utilization; $r_v$ Denotes the relative evaporation rate within the ambient zones and at the spray front; $\phi$ is the equivalence ratio; and $r_{w i}$ indicates the relative evaporation rate across different regions of the wall-surface flow.

This final phase deals with the slow oxidation of any remaining fuel and soot particles. The model uses switching functions ( $\varphi_o, \varphi_1, \ldots$ etc.) to smoothly transition between these phases, ensuring a realistic representation of the entire heat release profile, which is vital for accurately predicting engine performance and emissions.

3.3 NO_x formation modeling

Nitrogen dioxide (NO₂) and Nitric oxide (NO) are typically formed in combination, contributing to NOₓ emissions. In this study, the Zel’dovich mechanism is employed within the Diesel-RK simulation to model their formation [26]:

$O_2 \leftrightarrow 2 O$ (11)

$\mathrm{O}+\mathrm{N}_2 \leftrightarrow \mathrm{NO}+\mathrm{N}$ (12)

$\mathrm{O}_2+\mathrm{N} \leftrightarrow \mathrm{NO}+\mathrm{O}$     (13)

The concentration of atomic oxygen impacts the rate of reaction, as presented in Eq. (13). The volumetric concentration of NO is then calculated using the following expression:

$\frac{d[\mathrm{NO}]}{d \theta}=\frac{e^{-38020 / T_z}\left[\mathrm{~N}_2\right]_e[\mathrm{O}]_e\left(1-\left(\frac{[\mathrm{NO}]}{[\mathrm{NO}]_e}\right)^2\right) * 2.33 * 10^7 \mathrm{P}}{R T_z\left[1+\left(2365 / \mathrm{T}_z\right) e^{3365 / \mathrm{T}_z[\mathrm{NO}]} /\left[\mathrm{O}_2\right]_e\right]}\left[\frac{1}{r p s}\right]$  (14)

Nitrogen oxide emissions are primarily formed through the thermal (Zeldovich) mechanism, which is exponentially temperature-sensitive. WTO blends produce lower peak temperatures due to longer ignition delays and LHV, as captured by the temperature-dependent model, explaining the observed NO_x reductions.

The model implements the extended Zeldovich mechanism Eqs. (11)–(13) to calculate the rate of NO formation in each zone Eq. (14). The key term in Eq. (14) is the exponential factor [3], which means that a little rise in the local zone temperature $\left(T_Z\right)$ results in an enormous increase in the NO formation rate. This explains why any parameter that increases peak combustion temperatures (like a higher CR) will disproportionately increase NO_x emissions.

3.4 Soot formation modeling

Soot is a fine black carbon particle suspended in the vapor phase, typically generated by the incomplete combustion of hydrocarbons. Soot grains form, grow, and oxidize during combustion. A comprehensive analysis of soot simulation is presented [24].

The concentration of soot in the exhaust, under standard circumstances, can be expressed as shown below:

$[C]=\int_{\theta_B}^{480} \frac{d[C]}{d \tau} \frac{d \theta}{6 n}\left[\frac{0.1}{P}\right]^\gamma$   (15)

The Hartridge smoke level is estimated using the following equation:

Hartridge $=100 *\left[1-e^{(-24226[C])} * 0.9545\right]$ (16)

Particulate matter (PM) is described by Eq. (17) as a function of the Bosch smoke number (BSN):

$[P M]=565 *\left[\ln \frac{10}{10-\text { Bosch }}\right]^{1.206}$ (17)

The summary emission (SE) equation of air pollutants is another key relation that integrates both PM and NO_x emissions [24]:

$S E=C_{P M}\left[\frac{P M}{0.15}\right]+C_{N O}\left[\frac{N O_x}{7}\right]$       (18)

WTO's higher aromatic content and lower H/C ratio influence the nucleation and growth terms, explaining the 2.44% and 6.32% increases in smoke.

Hence, soot formation is a complex nucleation, surface growth, and oxidation process, often modeled empirically in engine simulations. The model used here, based on the study [24], calculates the net soot formation rate in each zone (Eq. (15)). The Hartridge smoke unit (HSU) and BSN are then derived from the computed soot concentration (Eqs. (16) and (17)). Fuels with a higher carbon-to-hydrogen ratio or those that lead to fuel-rich zones (due to poor mixing) promote soot formation. The SE index, Eq. (18), is a valuable metric because it combines the environmental impacts of PM and NO_x into a single value, acknowledging that optimizing for one pollutant often worsens the other.

3.5 Software validation

The reliability of any simulation study fundamentally depends on its validation against empirical data. Before conducting the parametric analysis, the Diesel-RK model was carefully calibrated and validated to ensure the physical accuracy of its predictions. The software was configured to replicate the engines and operating conditions documented in three independent experimental investigations [23, 24], with the corresponding engine specifications summarized in Table 3. The validation procedure employed identical configurations and boundary conditions stored within the software’s database, enabling direct comparison with the experimental results reported in previous researches [9, 24]. All validation engines operated on conventional diesel fuel. The progression of in-cylinder pressure and spray development with respect to crank angle is illustrated in Figures 2 and 3, respectively. These comparisons reveal strong convergence between simulated and experimental data, with only minor discrepancies. The consistently low variance underscores the robustness of Diesel-RK as a tool for modeling fuel combustion in internal combustion engines.

Table 3. Characteristics of the three engine configurations utilized for verification [27-30]

The validation concentrated on two principal outputs: spray evolution and in-cylinder pressure profiles. Figures 2 and 3 show that the simulated results closely match the experimental findings. The spray penetration and morphology (Figure 2) and the pressure–crank angle relationship (Figure 3) is nearly indistinguishable from the experimental benchmarks.

Figure 2. Validation of spray profile versus crank angle

Figure 3. Validation of cylinder pressure versus crank angle

Quantitative comparison between numerical predictions and experimental data shows strong agreement. The deviation in peak cylinder pressure remained below 3.5%, while the error in BSFC was within 4.2%. These values fall within the acceptable range reported in similar numerical studies of compression-ignition engines, confirming the reliability and predictive capability of the Diesel-RK model for parametric investigations of alternative fuels.

This comprehensive numerical investigation successfully deciphers the complex interplay between the WTO blends and CR in a diesel engine, delivering actionable insights for its potential implementation. The core conclusions are:

1. Inherent fuel trade-off: WTO blends introduce a fixed trade-off: a substantial diminution in NO_x emissions (up to 10.13% for a 20% blend) is achieved at the direct expense of higher fuel consumption (+3.32%) and increased smoke emissions (+6.32%). This is primarily driven by the fuel's lower energy content and poorer ignition quality, which delays and cools the combustion process.

2. CR as a powerful tuner: It is highly effective for manipulating the WTO's combustion behavior. Raising the CR aggressively counteracts its main weakness, slashing ignition delay by 25.4% by providing a hotter, more reactive in-cylinder environment.

3. The calibration dilemma: Using a high CR to improve combustion unlocks a second, more severe trade-off. The resulting high temperatures lead to a 21.9% increase in NO_x emissions, nullifying the WTO's inherent NO_x advantage and increasing the overall emissions footprint (SE index).

4. Performance paradox: For the 20% WTO blend, fuel consumption and thermal efficiency worsened as CR increased. This indicates that the optimal efficiency point for this fuel is at a CR below standard, likely due to increased heat transfer and friction losses at high CRs that outweigh combustion gains.

5. Strategic implementation framework: This study concludes that no optimal setting exists. The choice is application-specific:

6. For NO_x-sensitive applications: Use a higher WTO blend (20%) at a low to medium CR to capitalize on its inherent NO_x-reduction benefits, while accepting higher fuel consumption and smoke.

7. Use a lower WTO blend for performance-sensitive applications or avoid high CRs to maintain fuel efficiency.

8. For a balanced approach, a medium WTO blend (10%) at the standard CR may offer a reasonable compromise.