Open Access
Issue
Sci. Tech. Energ. Transition
Volume 79, 2024
Article Number 16
Number of page(s) 18
DOI https://doi.org/10.2516/stet/2024015
Published online 18 March 2024

© The Author(s), published by EDP Sciences, 2024

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Nomenclature

BSFC: Brake-specific fuel consumption

BTE: Brake thermal efficiency

CI: Compression-ignition

CO: Carbon monoxide

CO2 : Carbon dioxide

CR: Compression ratio

HC: Unburned hydrocarbon

ICE: Internal combustion engine

NOx: Oxides of nitrogen

PM: Particulate matter

SOME: Safflower oil methyl ester

1 Introduction

For over a century, internal combustion engines have served as indispensable components in the propulsion of vehicles and machinery, deriving power from fossil fuels like gasoline, diesel, and natural gas through combustion for energy generation and mechanical work [1]. Despite their contributions, ICEs have environmental consequences, notably in climate change and air pollution, releasing CO2 emissions and pollutants like NOx and particulate matter [2].

The increasing concern regarding the adverse environmental consequences and potential health hazards of fossil fuels has stimulated a heightened interest in transitioning towards cleaner and more sustainable alternatives, notably electric vehicles and renewable energy sources [3, 4]. Despite the emergence of alternative engine technologies, the complete replacement of ICEs with alternative models is a time-consuming process requiring substantial investments in new technologies and infrastructure [5]. Therefore, enhancing the efficiency of ICEs, addressing environmental impact, and minimizing greenhouse gas emissions through adopting alternative fuels are crucial priorities for engine developers and scientists [68]. Various alternative fuels, including biodiesel, ethanol, hydrogen, natural gas, propane, and electricity, present distinct characteristics, benefits, challenges, and limitations [9]. Integrating some of these fuels into existing engines requires substantial modifications to the engine’s design, posing a challenging and time-consuming task [10]. Therefore, scientists and researchers are diligently investigating innovative solutions to improve the fundamental characteristics of ICEs without requiring fundamental alterations to existing engine models [11, 12].

Biodiesel, a renewable alternative fuel derived from organic sources such as vegetable oils and animal fats, has recently garnered considerable attention as a sustainable solution for ICEs [13]. The prominence of this biofuel is underscored by its capacity to address critical challenges associated with conventional fossil fuels. A primary distinguishing feature of biodiesel is its reduced environmental impact compared to traditional petroleum-based counterparts [14]. Through a process known as transesterification, these renewable feedstocks are converted into biodiesel, leading to a fuel that exhibits lower levels of harmful emissions during combustion, particularly in terms of carbon dioxide [15]. Another notable advantage of biodiesel lies in its inherent compatibility with standard engine infrastructure, facilitating its use in conventional diesel engines with minimal modifications [16]. Moreover, beyond its intrinsic status as a renewable fuel, biodiesel presents a promising prospect due to its potential to diversify fuel sources and reduce dependence on non-renewable fossil fuels.

The production of biodiesel encompasses a diverse range of sources, spanning both conventional and unconventional origins [17]. Traditional sources involve vegetable oils such as soybean, canola, palm, sunflowers, and animal fats like beef tallow and pork lard [18]. The selection of the biodiesel source is contingent upon considerations such as availability, cost, and environmental impact. Thus, advancing new sources and technologies becomes imperative in guaranteeing a sustainable and reliable biodiesel supply for the future [1921].

Vegetable oils have emerged as a focal point of research within biodiesel production, capturing considerable interest due to their inherent characteristics of renewability and biodegradability [22]. The appeal of vegetable oils lies in their renewability, as they are produced from crops that can be replanted and cultivated regularly [23]. Additionally, the biodegradability of these oils aligns with the growing emphasis on environmentally friendly fuel sources, as they break down naturally over time, minimizing their impact on ecosystems [2426].

The conversion of vegetable oils into biodiesel is achieved through a chemical process called transesterification. This chemical reaction involves reacting triglycerides present in vegetable oils with an alcohol, typically methanol or ethanol, facilitated by a catalyst, often sodium or potassium hydroxide. The reaction results in the production of biodiesel (fatty acid methyl or ethyl esters) and glycerol as a byproduct [27]. The success of the transesterification process is influenced by several factors, including feedstock selection, the molar ratio of alcohol to triglyceride, type of catalyst used, reaction temperature, and reaction time [28]. Optimizing these parameters is crucial to achieving high biodiesel yields and maintaining the overall efficiency of the production process.

Safflower oil has emerged as a novel and noteworthy source in biodiesel production, capturing the attention of researchers. Safflower oil, derived from the seeds of the safflower plant (Carthamus tinctorius L.), is a vegetable oil belonging to the sunflower family. The safflower plant is predominantly cultivated for its oil, serving various purposes such as culinary applications, skincare, and biodiesel production [29]. Furthermore, the safflower plant boasts a high oil yield per acre and thrives in semi-arid and arid regions, rendering it a promising and sustainable choice for biodiesel production [30]. Recent empirical investigations have revealed that safflower oil biodiesel (SOME) exhibits favorable fuel properties, including a high cetane number and low viscosity, compared to traditional diesel fuel [31, 32].

1.1 Literature survey

The scientific literature abounds with various investigations that have explored the utilization of alternative fuels derived from vegetable oils in both spark-ignition and compression-ignition engines. As previously mentioned, safflower oil has emerged as a prospective feedstock for biodiesel production, attracting the interest of numerous researchers. These studies have provided insights into the influence of safflower oil biodiesel on engine performance and emissions.

In a recent study conducted by Gongora et al. [33], the effects of safflower biodiesel utilization on the performance and emissions of a turbocharged diesel engine were investigated. The study revealed that safflower biodiesel exhibited a higher cetane number of 56.6 compared to the commercial diesel range of 53.2–54.2. Furthermore, using safflower biodiesel resulted in a notable reduction of 12%, 34%, and 10% in CO, HC, and PM emissions, respectively, compared to commercial diesel. Additionally, safflower biodiesel demonstrated a slightly lower BSFC rate of 222 g/kWh compared to commercial diesel, which ranged between 224 and 226 g/kWh. Singh and Tirkey [34] conducted an experimental investigation to optimize a single-cylinder CI engine’s performance and emission characteristics using waste safflower oil biodiesel and its blends with diesel fuel. The study revealed that the waste safflower oil biodiesel blends improved engine performance and reduced emissions compared to fossil diesel. The optimized blend, consisting of 20% waste safflower oil biodiesel and 80% diesel, demonstrated the highest BTE of 31.68% and the lowest BSFC of 0.276 kg/kWh. Regarding emissions, the optimized blend exhibited a reduction of 32.33%, 22.97%, 4.91%, and 20.85% in CO, HC, NOx, and PM, respectively, compared to pure diesel. The impact of SOME and diesel blends on the performance and emissions characteristics of a CI engine was investigated In an experimental study conducted by Venkatesan et al. [35]. The study showed that the addition of SOME to diesel fuel led to an improvement in BTE and BSFC. The highest BTE was observed at a blend ratio of 20% SOME and 80% diesel, while the lowest BSFC was observed at a blend ratio of 10% SOME and 90% diesel. Additionally, the study found that incorporating SOME into diesel fuel resulted in lower CO and HC emissions, although NOx emissions slightly increased. The lowest CO and HC emissions were observed at a blend ratio of 20% SOME and 80% diesel, while the lowest NOx emissions were observed at a blend ratio of 10% SOME and 90% diesel. The physicochemical properties of the SOME were also characterized, revealing higher viscosity and density than diesel fuel. Ilkılıç et al. [36] explored the utilization of safflower oil as a feedstock for biodiesel production and its application in a diesel engine. The engine’s performance and emissions were evaluated using safflower biodiesel on a single-cylinder, direct injection, water-cooled diesel engine. Incorporating SOME into the diesel engine resulted in a slight increase in BSFC compared to conventional diesel fuel. However, it notably decreased CO and PM emissions by 70% and 60%, respectively. Although there was a slight increase in NOx and HC emissions. Celebi and Aydin [37] conducted a research study to explore the impact of adding butanol into safflower biodiesel on the engine’s performance and emissions. The experiments were carried out using a single-cylinder diesel engine with various fuel blends, including B0 (100% diesel), B10, B20, B30, and B40 (10–40% safflower biodiesel), each containing different proportions of safflower biodiesel and diesel fuel. The findings revealed that the optimal fuel blend was B30, exhibiting the highest BTE at 33.6% and the lowest BSFC at 0.273 kg/kWh among all the tested blends. As for emissions, the study observed a decrease in CO and NOx with an increase in butanol content, while smoke opacity decreased with the rise in safflower biodiesel content in the fuel blends. Aydogan [38] investigated the effects of bioethanol, safflower biodiesel, and diesel fuel blends on a common rail diesel engine’s performance, combustion, and emission characteristics. The findings indicated that incorporating bioethanol and safflower biodiesel into diesel fuel improved the engine’s BTE and reduced CO and HC emissions. However, adding bioethanol and biodiesel to the fuel blend increased NOx emissions. The optimal fuel blend was identified as B10E5 (10% biodiesel, 5% bioethanol, and 85% diesel), exhibiting the highest BTE and the lowest CO and HC emissions. In another experimental investigation conducted by Aydin and Ogüt [39], four different fuel blends were tested using a single-cylinder diesel engine: B0 (100% diesel), B5 (5% ethanol, 5% biodiesel, and 90% diesel), B10 (10% ethanol, 10% biodiesel, and 80% diesel), and B15 (15% ethanol, 15% biodiesel, and 70% diesel). The findings indicated that the B10 fuel blend exhibited the best engine performance, showing a 6.4% reduction in BSFC and a 5.5% increase in BTE compared to the B0 mixture. Incorporating biodiesel and ethanol led to lower CO and HC emissions, while NOx emissions experienced a slight increase. The lowest emissions were observed with the B10 blend, demonstrating a 47% reduction in CO emissions and an 18% reduction in HC emissions compared to the B0 blend. However, using the B10 blend resulted in a 6.7% increase in NOx emissions compared to the B0 mixture. An experimental-numerical investigation conducted by Aydin [40] assessed the combustion, performance, and emission characteristics of a generator diesel engine fueled by safflower biodiesel-kerosene blends. The experiments were carried out at varying engine loads and fuel blend ratios. The study observed that the BTE decreased with an increase in safflower biodiesel content in the fuel blend while the BSFC increased. The emissions of CO, HC, and PM were found to be lower for safflower biodiesel blends compared to kerosene. However, the NOx emissions increased with the rise in safflower biodiesel content. Additionally, adding kerosene to safflower biodiesel improved fuel properties, such as cetane number, viscosity, and density. The numerical comparison of the results exhibited good agreement with experimental data. A research study conducted by Özçelik [41] investigated the influence of safflower biodiesel-euro diesel fuel blends on engine performance and emissions using a diesel engine. The study tested four fuel blends, B0, B5, B10, B15, and B20, with varying biodiesel percentages ranging from 5% to 20%. The B20 blend exhibited the highest BTE and the lowest BSFC among these blends. Furthermore, the study revealed that the B20 blend resulted in the lowest CO and HC emissions, demonstrating reductions of 46.7% and 29.2%, respectively, compared to B0. However, using the B20 blend led to a 7.2% increase in NOx emissions compared to B0. Ors and Bakircioglu [42] performed an extensive experimental-numerical examination to assess the impact of safflower oil biodiesel on the performance and exhaust emissions of a CI diesel engine. The biodiesel was produced through a meticulous two-step transesterification process, with its properties systematically characterized following ASTM standards. Various blends of safflower oil biodiesel with diesel fuel (B10, B20, and B30) were employed during engine operation. The findings indicated that the utilization of safflower oil biodiesel blends led to decreased emissions of HC, CO2, and CO, albeit with elevated BSFC, NOx, and PM emissions compared to pure diesel fuel. Additionally, the researchers employed a numerical modeling technique, leveraging Artificial Neural Networks (ANN), to predict engine performance and emissions. They reported a satisfactory alignment between the modeling predictions and the experimental observations, affirming the reliability of their approach.

The consolidated outcomes in the scholarly literature culminate in a discernible conclusion: using safflower biodiesel in diesel engines can significantly influence both engine performance and emissions. Furthermore, safflower biodiesel exhibits a higher cetane number than conventional diesel fuel, indicating heightened ignition proficiency. Consequently, safflower biodiesel’s accelerated ignition and enhanced combustion efficiency contribute to heightened engine power and torque output. Moreover, the elevated lubricating properties of safflower biodiesel provide potential advantages in alleviating engine wear and extending engine longevity.

The scientific community recognizes the inherent complexities and the temporal and financial constraints of conducting numerous experimental studies on internal combustion engines. These challenges arise from the necessity for specialized equipment, high experiment costs, and the time-intensive nature of data collection and analysis [43]. Accordingly, the importance of computer-based numerical studies and engine simulation methodologies has grown significantly in recent years. These innovative approaches present a viable alternative, promising enhanced cost-effectiveness, reduced time commitments, and heightened accuracy [44]. Concurrently, significant advancements have occurred in developing specialized computer software designed specifically for modeling and analyzing internal combustion engines. These software tools have been developed for modeling and analyzing internal combustion engines, ranging from basic analytical models to sophisticated simulation packages integrating detailed physics-based engine process models [45].

Simulation methodologies have become indispensable tools for researchers and engineers studying alternative fuels and assessing their impacts on internal combustion engines. These computational tools streamline testing processes, facilitating the determination of optimal blend ratios and operating parameters for various fuel combinations, thereby reducing the costs and time associated with evaluating different fuels [46, 47]. The AVL simulation software is prominently acknowledged as a comprehensive instrument to simulate internal combustion engines, garnering notable recognition within the automotive industry and academic research community [48]. This software provides an invaluable platform for the intricate simulation and analysis of internal combustion engines, offering advanced models encompassing various aspects of thermodynamics, combustion, and fluid dynamics [49]. Furthermore, the software facilitates a systematic evaluation of the impacts arising from diverse fuel compositions and various engine configurations on performance and emissions. This capacity empowers engineers and researchers to conduct exhaustive analyses, resulting in meticulous forecasts of engine performance and emission levels. The prognostications derived from these analyses are crucial in strategically discerning and selecting efficient fuel alternatives [50].

1.2 The novelty and goals of this study

The essentiality of transitioning from conventional fossil fuels to alternative energy sources arises from environmental, economic, and sustainability considerations. Moreover, the existence of numerous alternative sources necessitates extensive laboratory analyses to evaluate the effects of these alternative fuels on the performance and emission characteristics of internal combustion engines. Nevertheless, the substantial temporal and financial investments required for conducting experimental studies pose impediments, causing a deceleration in the examination process of these alternative fuels. Accordingly, numerical methods and computer-based simulations emerge as pivotal tools in addressing these challenges.

This study aims to employ AVL simulation software for modeling and analyzing the performance and emissions of a Compression Ignition (CI) engine fueled with methyl ester synthesized from safflower seed oil. Moreover, the simulation results are juxtaposed with data obtained from an experimental study conducted at Kırıkkale University, Türkiye, in 2022 [24]. This comparative investigation is designed to comprehensively understand the concordance or divergence between simulated outcomes and the data observed through practical experimentation. The application of this simulation methodology facilitates a comprehensive evaluation of engine performance and emissions, contingent upon the validation of results and confirmation of the simulation’s precision. The numerical methods of this nature yield insights that may not be inherently obtainable through experimental tests or necessitate significant investments in time and resources.

2 Material and methods

The present study analyzed the performance and emission characteristics of a single-cylinder, water-cooled CI engine operating on safflower oil methyl ester as an alternative to traditional diesel fuel. The simulation process utilized the AVL simulation software, encompassing a range of compression ratios (CR) (varied between 12:1 and 18:1) and engine loads (ranging from 25% to full load). Following the simulation phase, the acquired results were compared with the outcomes derived from an experimental study conducted under identical conditions [24].

2.1 Experimental case

Based on the experimental study, diesel fuel and SOME were utilized in a single-cylinder, four-stroke, direct-injection, water-cooled CI engine, specifically emphasizing their effects on performance and exhaust emission characteristics. The investigation was carried out under various engine loading conditions, ranging from 25% to full load in increments of 25% while maintaining a constant speed of 1500 rpm. Additionally, the CR was adjusted between 12:1 and 18:1 to observe its effects on engine performance and emission characteristics. The engine’s maximum output power was 5.2 kW, and its cylinder volume was 661.5 cc. The technical specifications of the research engine, tabulated in Table 1, were employed in the simulation. Moreover, the schematic diagram of the experimental test rig used in the research is presented in Supplementary Figure 1.

Table 1

Technical specifications of the research engine [24].

An overview of the physicochemical characteristics of SOME and reference diesel fuel are shown in Table 2.

Table 2

Physicochemical properties of SOME and reference diesel fuel [24]

2.2 Engine simulation

The current investigation used AVL simulation software to model a single-cylinder, four-stroke, CI diesel engine. The BOOST tool within the AVL simulation program was employed, as it is designed explicitly for engine simulations and offers advanced models and algorithms that accurately capture the complex dynamics of engine operation. The engine model represented a valuable tool for investigating the engine’s behavior under various operating conditions, such as different engine speeds, loads, and CRs. The engine simulation model generated through the AVL software is presented in Figure 1.

thumbnail Fig. 1

Schematic model of test engine generated by AVL software (SB1: System input boundary condition; SB2: System output boundary condition; E1: Engine; C1: Cylinder; I1: Injector; CL1: Air cleaner; PL1, PL2: Plenum; R1, R2, R3: Restrictions; MP1, MP2, MP3, MP4, MP5, MP6, MP7, MP8: Measuring points).

The following is a detailed description of the procedures required to create an accurate simulation model of the test engine [52]:

  1. The initial stage in developing a precise simulation model involves meticulously selecting essential engine components derived from the Components Tree. These components encompass a multitude of components, including the engine block, cylinder head, valves, fuel injection system, and exhaust system.

  2. The subsequent step entails delineating the technical specifications for each component. This encompasses specifying the cylinder dimensions, injector specifications, combustion model, air/fuel ratio, engine speed, CR, and other pertinent parameters.

  3. The third procedural stage involves specifying appropriate boundary conditions. This encompasses the definition of ambient pressure, temperature, and initial values of gases to establish a comprehensive framework for the simulation model.

  4. The final step involves inputting the physical-chemical information of the fuel compounds. This encompasses specifying the fuel type, chemical composition, and other relevant parameters.

Accurately defining all these parameters ensures the simulation model’s capability to faithfully replicate the engine’s behavior and performance across varied conditions.

The engine simulation model’s construction commenced with the assembly of essential components, encompassing the engine block, cylinder, fuel injectors, and plenums. These components were interconnected with pipes to constitute a comprehensive system. Each component was individually modeled with distinct specifications, encompassing dimensions, types, and operational characteristics. These details played a crucial role in faithfully simulating the engine’s performance under diverse operating conditions. Following the placement of components, the software received comprehensive information about each element, incorporating cylinder and injector specifications. This involved specifying intricate details such as cylinder dimensions, bore, stroke, and capacity, significantly influencing the engine’s performance. Similarly, meticulous adjustments were made to fuel injector details, including the fuel-air composition percentage and fuel injection rate, to align with actual injector specifications, ensuring precise simulation of the fuel injection process. In addition to specifying component details, the software also incorporated considerations for boundary layer conditions influencing the engine’s performance. These factors encompassed parameters like the initial temperature and pressures of both input and output boundary layers, meticulously adjusted to replicate the genuine operational environment of the engine.

During the modeling process, it was necessary to determine the characteristics of combustion. A heat loss approach was chosen, relying on the simulation program database. Specifically, a Vibe function was selected as the combustion model according to the proposal provided by AVL for diesel engines [51]. The Vibe function is a mathematical model grounded in the heat diffusion approach employed to simulate the combustion process in diesel engines. The effectiveness of this model lies in its capability to accurately predict various combustion characteristics of diesel engines, encompassing ignition delay, combustion duration, and heat release rate, by considering parameters such as fuel injection timing, combustion chamber geometry, and fuel properties. This combustion model commonly employs the following Vibe function (1) to estimate the authentic heat release characteristics during the engine combustion [5153]:(1) (2) (3)where, Q [J] is the total fuel heat, m [–] is the shape parameter of the vibe function, and a [–] denotes the Vibe parameter, which can be accepted to be 6.9 for complete combustion. Additionally, α [degrees], α0 [degrees], and Δαc [degrees] define the crank angle, the crank angle at the start of combustion and combustion duration, respectively.

The determination of the fraction of the fuel mass burned since the initiation of combustion involves the integration of the Vibe function:(4)where, x [–] is the mass fraction of burned fuel.

Following the selection of the combustion process simulation model and the input of relevant particulars, the subsequent stage was configuring the emissions simulation model. Emissions simulation is executed within the species setup section of AVL Boost software, serving as a critical component for pollutant analysis. It encompasses two sub-classifications as follows: classic species setup and general species transport. The first mode is operated when a single fuel type is utilized, facilitating system performance analysis. Conversely, the general species transport mode provides a more detailed examination of pollutants generated during the combustion process. The general species transport mode facilitates the analysis of seven distinct emission parameters, including HC, NOx, CO2, CO, and PM, which have substantial environmental implications. The examination of these components occurs within each relevant element of the model, adhering to the law of conservation of mass and mass fractions. This implies that the model considers the quantity of each component present in the system and tracks its changes over time due to the combustion process. AVL Company provides various models and equations for simulating emissions and pollutant gases within the general species transport mode. Utilizing these models and equations enables a comprehensive understanding of the pollutants generated during the combustion process [51, 52].

In this study, the proposal introduced by the AVL Company was incorporated to consider the oxidation of moist CO in the CO production model during the emission analysis. The CO formation model utilized in the Boost tool is derived from the research conducted by Onorati et al. [54], employing numerical simulations of two chemical reactions (5) and (6) involved in CO production during combustion to predict CO emissions accurately. The chemical reactions and numerical models associated with CO production/destruction are presented in Table 3.

Table 3

CO production/destruction model [51].

The total rate of CO production/destruction, expressed in [mole/cm3s], is determined by the following equation: (9) (10)where, cCO,act [mol] indicates the actual concentration of CO, cCO,equ [mol] refers to the predicted equilibrium concentration of CO, r [mole/cm3s] shows the production/destruction rate of CO, and T (°C) is the mean gas temperature.

The HC production model incorporates critical considerations, including the crevice mechanism, HC absorption/desorption mechanism, and HC post-oxidation. The crevice mechanism pertains to small areas where combustion may be limited due to restricted access to fuel and air, such as gaps between the piston and the cylinder [55]. The HC absorption/desorption mechanism involves the adsorption and desorption of HC molecules onto and from the surfaces of the cylinder and piston [56]. HC post-oxidation occurs after the primary combustion event, where residual oxygen reacts with HC molecules, producing additional emissions [57]. To address these factors, AVL Boost utilizes various techniques, including applying the Arrhenius equation to capture slow HC post-oxidation accurately. The model assumes uniform pressure within the cylinder and crevices, equivalent piston temperatures, and uniform mass inside the crevice volumes. However, a crucial consideration is the impact of heat transfer from the walls on flame propagation in crevices, which can impede combustion and lead to HC production. The estimation of the quantity of mass present in the crevices at any given time is a crucial aspect of the model [51, 52]:(11)where, mcrevice [kg], P [Pa], V [m3], M [kg/kmol], R [J/(kmol.K)], and Tpiston [K] define the quantity of unburned fuel present in the crevices, the pressure within the cylinder, the total volume of the crevices, the molecular weight of the unburned charge, the gas constant, and the piston temperature, respectively.

Due to the heightened temperature within the combustion chamber, hydrocarbons released into the burned gases undergo a complex oxidation mechanism [51]. To streamline this intricate process, Lavoie and Blumberg proposed a condensed method utilizing an Arrhenius equation (12) that considers slow HC post-oxidation [58].(12)where, C [kmole/m3] represents the concentration of HC and O2, Fox [–] denotes the multiplier for the post-oxidation of HC, f [–] is the scaling factor for post-oxidation, T [K] indicates the piston temperature, TOx [K] represents the activation temperature (taken to be 18 790.0), and Aox [m3/kmole/s] signifies the frequency factor (assumed to be 7.7E12).

The presence of lubricating oil in the fuel or adhered to the walls of the combustion chamber is a well-known issue contributing to hydrocarbon emissions during the combustion process. Lubricating oil may enter the combustion chamber through various means, including oil leaks or blow-by, and can contribute to hydrocarbon formation during combustion. When fuel is burned, the absence of fuel vapor in the burned gases causes fuel vapor to be absorbed from the liquid oil by the burned gases, leading to the production of hydrocarbons. The distribution of the fuel mass fraction in the oil film can be obtained by solving the diffusion equation (13) [51, 52].(13)where, WF [–], t [s], r [m], and D [m²/s] define the mass fraction of fuel in the oil film, time, the radial position in the oil film, and the relative diffusion coefficient, respectively. The calculation of the diffusion coefficient can be achieved using the following formula:(14)where, M [g/mol] represents the molecular weight of the oil, T [K] denotes the temperature of the oil, Vf [cm3/mol] is the molar volume of the fuel under normal boiling conditions, and μ [cSt] denotes the viscosity of the oil.

The AVL Boost software incorporates a NOx production model that utilizes the well-known Zeldovich mechanism [51]. The Zeldovich mechanism, introduced by Y.B. Zeldovich in 1946, outlines a chemical reaction pathway describing the formation of NOx in high-temperature and high-pressure combustion systems, as encountered in internal combustion engines [59]. Pattas and Hafner further refined the Zeldovich mechanism, and their work serves as the foundation for the NOx production model in the AVL Boost software [60]. The NOx prediction model developed by Pattas and Hafner is provided in Table 4. This model forecasts NOx by numerically simulating six chemical reactions that are involved in NOx formation in the combustion. Chemical reactions (15)(18) are based on the Zeldovich mechanism, representing the primary pathway for NOx formation during combustion in the presence of oxygen and fuel. Additionally, reactions (19) and (20) are included as N2O production factors in combustion, as suggested by Pattas and Hafner [51, 52].

Table 4

The NOx prediction model by Pattas and Hafner [51].

The prediction of NOx emissions involves utilizing engine-related data, encompassing fuel specifications, engine speed, and in-cylinder temperature and pressure, which are inputted into numerical equations (21)(26). Where, k0, a, and T A represent constants and initial values utilized in the equations examined by Pattas and Hafner during their NOx modeling efforts.

The NO production/destruction rate can be calculated in [mole/cm3s] units using the following formula:(27) (28) (29) (30)

Moreover, the concentration of N2O can be computed using the following equation:(31)where, CPostProcMult [–] represents the post-processing multiplier of NOx, whereas CKineticMult [–] denotes the kinetic multiplier of NOx. The variables p [MPa] and T [K] signify the cylinder pressure and temperature, respectively. The reaction rate of the Zeldovich mechanism is denoted by r [kmole/cm3]. Additionally, Cact [mol] and Cequ [mol] correspond to the actual and predicted equilibrium concentrations of NOx, respectively.

Once the necessary parameters for simulating the combustion process and emission modeling were established, the subsequent step involved determining the fuels used in the study within the AVL Boost software. By default, the software includes diesel fuel and various other standard fuels. Additionally, the “Gas Properties Tool” integrated within the AVL software enables the definition of specific fuels for use during the simulation process. The physicochemical properties of the SOME fuel, encompassing viscosity, density, calorific value, molecular weight, as well as oxygen, hydrogen, and carbon content, were incorporated into the software using laboratory data. Consequently, the AVL software defined SOME as a new fuel. Subsequently, the software was configured with the general simulation requirements, including engine speed, CR, cycle duration, and other pertinent parameters. The experimental study informed the selection of simulation parameters to ensure the precision and applicability of the simulation outcomes. After configuring the necessary parameters, the engine simulation was carried out under various engine load conditions, ranging from 25% to full load with 25% increments, while maintaining a constant speed of 1500 rpm. Moreover, the simulation was repeated for CR values ranging from 12:1 to 18:1 to evaluate the impact of CRs on engine performance and emission characteristics. Upon completing the simulation process, the AVL software conducted a comprehensive analysis to compute key engine performance parameters, including engine power, fuel consumption, and emission values such as CO, CO2, NOx, and HC. Finally, a comparative analysis was conducted by comparing the results with laboratory data obtained from the experimental study to validate the accuracy of the simulated engine outcomes.

3 Results and discussion

This numerical study aimed to assess the impact of SOME on engine performance parameters and emissions, with a specific focus on BSFC, CO, CO2, HC, and NOx emissions utilizing an engine simulation approach. Subsequently, comprehensive engine tests were meticulously conducted on a model generated through AVL simulation software. Additionally, a thorough comparison was performed between the simulation outcomes and the findings derived from a concurrent laboratory study conducted under identical operating conditions to augment the result’s accuracy and reliability.

The BSFC for both SOME and diesel fuel was acquired across various CRs and engine loads in the simulated engine, as illustrated in Figure 2. The engine simulation results indicate that the utilization of SOME fuel resulted in higher BSFC compared to diesel fuel, especially noticeable at lower CRs. These observations are congruent with the outcomes of literature studies, which ascribed the higher BSFC of SOME to its relatively lower heating value, increased density, and higher viscosity than diesel fuel [36, 42]. Celebi and Aydin [37] reported that the BSFC value of biodiesel increased by only 5–4% compared to diesel fuel. In a study by Gongora et al. [33], safflower biodiesel exhibited a slightly lower BSFC rate of 222 g/kWh compared to commercial diesel, which ranged from 224 to 226 g/kWh. Additionally, Ors et al. [61] observed a 13% increase in BSFC for SOME fuel compared to diesel fuel. Moreover, they noted a consistent rise in BSFC values at all CRs with a decreased engine load. The BSFC results exhibited a declining pattern with an increase in CR, regardless of the fuel type (diesel or SOME). Similarly, an increase in engine load was associated with a reduction in BSFC. Based on the simulation study results, the utilization of SOME led to an increase in BSFC by up to 11.4%. In contrast, the experimental findings demonstrated a slightly higher increment of up to 12.9% under identical conditions. The highest recorded BSFC value in the engine simulation was 0.495 kg/kWh for SOME fuel, observed at a CR of 12:1 and 25% of engine load. The experimental study carried out under similar conditions determined it to be 0.520 kg/kWh. Conversely, in the simulation study, diesel fuel’s lowest BSFC value was 0.267 kg/kWh at 18:1 of CR and full engine load. Under the same conditions, the experimental research recorded a 0.280 kg/kWh value. An analysis of simulation and experimental results revealed that reducing the CR narrowed the disparity in BSFC outcomes between the experimental and numerical studies, as depicted in Figure 3.

thumbnail Fig. 2

Variation of BSFC for SOME and diesel fuel concerning the engine load and CR.

thumbnail Fig. 3

Difference in BSFC outcomes between experimental and simulation studies concerning ranging CR and engine loads.

CO emissions emerge as byproducts from the imperfect combustion of carbon-rich fuels or excess oxygen in the combustion process [62]. Recent published studies emphasize that SOME exhibits a higher oxygen content than diesel fuel. This heightened oxygen content facilitates complete combustion, reducing CO emissions [38]. Furthermore, due to its lower carbon content, safflower biodiesel demonstrates the potential to mitigate CO emissions compared to diesel fuel [63]. Singh and Tirkey [34] determined that the CO emissions were 32.33% lower in SOME compared to diesel fuel under identical engine operational conditions. Similarly, Aydin and Ogüt’s [39] experimental findings indicate that SOME fuel demonstrates superior combustion efficiency compared to diesel fuel, resulting in a significant reduction of up to 46.7% in CO emission values. In another study by Celebi and Aydin [37], CO emissions decreased with an increase in the ratio of SOME content in fuel blends. This reduction was attributed to the higher cetane number and improved combustion characteristics of SOME fuel. The simulation results substantiated the findings mentioned above, indicating that the utilization of SOME significantly reduces CO emissions compared to diesel fuel, as depicted in Figure 4. Furthermore, the reduction was notably significant when employing higher CRs. Based on the findings of the simulation study, the utilization of SOME resulted in a notable decrease in CO emissions, reaching up to 43.1% compared to diesel fuel. At the same time, the experimental observations revealed a reduction of up to 39.53% under equivalent conditions. The engine simulation results indicated that CO emissions reached their minimum level of 0.13% for SOME when the CR was set at 18:1. Concurrently, the experimental investigation conducted under identical conditions reported a slightly higher CO emission level of 0.14%. Conversely, the simulated engine’s maximum CO emission reached 0.36% when using diesel fuel with a CR of 12:1. In contrast, the experimental study yielded a slightly elevated CO emission of 0.38% for this operational condition. Additionally, the disparities in CO emissions between the results of experimental and numerical studies were more pronounced at higher levels of CR and engine loads, as illustrated in Figure 5.

thumbnail Fig. 4

Variation of CO emission for SOME and diesel fuel concerning the engine load and CR.

thumbnail Fig. 5

CO emission difference between experimental and simulation studies at ranging CR and engine loads.

The simulation results revealed a rise of up to 7.3% in CO2 emissions when using SOME fuel compared to diesel fuel. This trend aligns with the outcomes of the experimental investigation, where a similar increase of up to 8.6% was observed, as depicted in Figure 6. Furthermore, it is crucial to highlight that a substantial increase in CR and engine load significantly contributed to the rise of CO2 emissions. The process of fuel combustion involves a chemical reaction with oxygen, producing CO2 as a byproduct [64]. According to the literature, this reduction could be attributed to the higher oxygen content of SOME compared to diesel, which increased the production of CO2 emissions [29]. Gongora et al. [33] found that employing SOME fuel led to a significant increase, up to 80%, in CO2 emissions, particularly noticeable at higher engine loads. Correspondingly, Özçelik [41] demonstrated that CO2 emissions rose in correlation with the percentage of SOME content in fuel blends, attributed to the higher oxygen content inherent in SOME fuel. Aligning with findings from previous studies, the simulated engine demonstrated its highest recorded CO2 emission value, reaching 11.20%, when operating with SOME fuel and a CR of 18:1. In contrast, the lowest CO2 emission value, measuring 3.79%, was observed with diesel fuel and a CR of 12:1. It is essential to note that the experimental study yielded slightly varied results under identical conditions, with CO2 emission values of 11.95% and 3.84% for SOME and diesel fuel, respectively. As illustrated in Figure 7, the CO2 emission results related to diesel fuel demonstrated a higher degree of concordance between the two investigations than those associated with SOME fuel. Moreover, a more robust correlation was discerned between the practical and simulation findings, particularly at lower engine loads.

thumbnail Fig. 6

Variation of CO2 emission for SOME and diesel fuel concerning the engine load and CR.

thumbnail Fig. 7

CO2 emission difference between experimental and simulation studies at ranging CR and engine loads.

The emission of HC is a consequence of incomplete fuel combustion within internal combustion engines, releasing unburned hydrocarbon compounds into the exhaust gases [65]. The simulation results indicate that the utilization of SOME led to a substantial reduction in HC emissions by up to 2 times compared to diesel fuel, notably in higher CRs. Similarly, in an investigation conducted by Özçelik [41], the utilization of SOME fuel resulted in a substantial drop in HC emissions, reaching up to 50%, particularly noticeable in the fuel blends with a higher amount of safflower oil biodiesel. In another study, Ors and Bakircioglu [42] documented a significant reduction of 86.65% in HC emissions for biodiesel compared to diesel fuel. They attributed this decrease to the elevated exhaust gas temperature, indicating an improvement in combustion efficiency facilitated by the high oxygen content inherent in biodiesel. According to the simulation outcomes, the HC emission rate was predominantly influenced by the CR, exhibiting a decreasing trend as the CR increased, as depicted in Figure 8. Specifically, a significant disparity of 75% in HC emissions was observed between the CRs of 18:1 and 12:1 in SOME. Conversely, both laboratory and simulation studies indicated that an increase in engine load corresponded to a rise in HC emissions. For instance, at a constant CR, HC emission levels of 48 ppm and 106 ppm were recorded for SOME fuel at 25% and 100% of engine loads, respectively. Furthermore, in the simulation study, the HC emissions reached their lowest level of 20.86 ppm when utilizing SOME fuel with a CR of 18:1 and an engine load of 25%. At the same time, the experimental study recorded a slightly higher emission of 21.7 ppm under identical conditions. Conversely, the simulation study documented the highest HC emission of 194 ppm when using diesel fuel with a CR of 12:1 and operating the engine at full load. Similarly, the experimental study yielded a higher HC emission of 199 ppm under the same conditions. Additionally, the disparity in HC emission results between experimental and numerical research decreased as the engine load increased, as illustrated in Figure 9.

thumbnail Fig. 8

Variation of HC emission for SOME and diesel fuel concerning the engine load and CR.

thumbnail Fig. 9

HC emission difference between experimental and simulation studies at ranging CR and engine loads.

NOx emissions are generated as a byproduct of the combustion process in engines, arising from the extreme temperatures and pressures experienced during combustion. According to scholarly literature, the specific composition of SOME fuel, which incorporates oxygen, contributes to an enhanced oxygen concentration during the combustion process. This heightened oxygen content leads to elevated NOx emissions compared to diesel fuel [66]. Ors and Bakircioglu [60] explained that using SOME fuel resulted in a notable increase in NOx emissions, reaching up to 1.3 times when compared to pure diesel. In another study by Praveena et al. [67], SOME usage yielded a modest 12.2% increase in NOx emissions. Similarly, Simsek’s findings [68] illustrated a significant increase of up to 80.50% in NOx emissions associated with using SOME fuel compared to pure diesel. The engine simulation results were also in line with the scholarly literature, as presented in Figure 10. According to the simulation results, the utilization of SOME led to a notable increase in NOx emissions, reaching up to 49.2%, compared to diesel fuel. Conversely, the experimental findings revealed a slightly higher increment of up to 53% under identical conditions. Additionally, the simulation results indicated that under operating conditions characterized by high CRs, the combustion process experienced elevated temperature and pressure. These conditions fostered optimal circumstances for the increase in NOx production. The simulated engine recorded the highest NOx emission at 1643 ppm when utilizing SOME at a CR of 18:1 under full engine load conditions. Conversely, the lowest NOx emission was observed as 99.4 ppm for diesel fuel at a CR of 12:1 and 25% engine load. Meanwhile, the experimental investigation reported slightly different values, with NOx levels of 1687 ppm and 103 ppm, respectively, for identical conditions. Furthermore, a higher degree of concurrence between the simulation results and the experimental measurements was observed as the engine load increased, as shown in Figure 11.

thumbnail Fig. 10

Variation of NOx emission for SOME and diesel fuel concerning the engine load and CR.

thumbnail Fig. 11

NOx emission difference between experimental and simulation studies at ranging CR and engine loads.

4 Conclusions

This paper utilized the BOOST tool of AVL simulation software to model a single-cylinder, compression-ignition engine fueled by safflower oil methyl ester and conventional diesel fuel. Engine performance and emission characteristics were evaluated across a spectrum of CRs ranging from 12:1 to 18:1 under diverse engine loads varying from 25% to full load. Subsequently, the simulation results were compared to the findings of an experimental study conducted under identical conditions. The principal results derived from the present numerical investigation can be briefly summarized as follows:

  • The utilization of safflower oil biodiesel resulted in an increase in BSFC compared to diesel fuel, which can be attributed to the lower heating value of SOME fuel. Furthermore, a reduction in BSFC was observed with an increase in CR. Conversely, higher engine loads led to lower BSFC. Moreover, SOME exhibited the highest BSFC, reaching 0.495 kg/kWh, when operating at a CR of 12:1 under an engine load of 25%. In contrast, pure diesel demonstrated the lowest BSFC, registering 0.267 kg/kWh, when operating at a CR of 18:1 under full engine load.

  • A substantial variation in CO emissions was observed, with the lowest recorded value of 0.13% for SOME, while the highest value of 0.36% was measured for pure diesel. Moreover, the investigation revealed a noteworthy reduction in CO emissions achieved by implementing higher CRs.

  • The oxygen content of SOME resulted in a slight increase in CO2 production compared to diesel fuel. Consequently, under identical CR and engine load, CO2 emissions increased by up to 2% relative to diesel fuel. Moreover, the increase in engine load demonstrated a direct association with the rise in CO2 emissions, while the impact of CR on CO2 emissions was relatively minor. The lowest recorded value of CO2 emission was 3.7% for diesel fuel under 25% of engine load. In contrast, the highest emission value of 11.2% was observed when SOME was utilized under full engine load conditions.

  • A substantial reduction in HC emissions was observed using SOME, particularly at lower engine loads and higher CRs. The impact of higher CRs on reducing HC emissions was significant, evidenced by a notable 74% difference in HC emission levels between CRs of 12:1 and 18:1. The minimum HC emissions of 20.8 ppm were recorded at a CR of 18:1, while diesel fuel emitted 63.8 ppm under identical conditions.

  • The increase in NOx emissions was directly correlated with the rise in CR and engine load. The NOx emissions were higher for SOME than diesel fuel at all operating conditions, primarily attributable to its higher oxygen content. Notably, SOME exhibited the highest NOx emission of 1643 ppm at a CR of 18:1 and full engine load, while diesel fuel demonstrated the lowest emission of 99 ppm at a CR of 12:1 and 25% engine load.

Through a comparison between simulation and experimental results, this study has validated the effectiveness of engine modeling techniques in accurately predicting engine performance and emission characteristics. The simulation results closely aligned with the experimental research findings, particularly when higher CRs were applied. Overall, as the CR increased, the simulation and experimental results showed convergence in terms of BSFC and emissions such as CO, CO2, and NOx. However, an opposite trend was observed in the case of HC emissions, where the simulation and experimental results diverged.

4.1 Prospects for future research

The overarching focus of this study is to Anticipate and examine the effects of employing alternative fuels on fuel consumption, engine efficiency, and emissions by applying advanced simulation methodologies. Within this framework, future research should focus on advancing simulation methodologies by integrating sophisticated multidimensional combustion and heat transfer models to enhance the accuracy of predicting combustion kinetics, heat release patterns, and emissions formation. Concurrently, emphasizing the application of transient simulations across various dynamic operating conditions could improve predictive capabilities, providing a realistic representation of engine responses to variables such as engine speed, CR, and engine load. Furthermore, adopting a simultaneous and integrated approach, wherein laboratory experiments and numerical simulations are conducted in parallel, will be advantageous for consistently validating and modifying simulation models.

Acknowledgments

The authors express their gratitude to AVL-AST, located in Graz, Austria, for their generous provision of the AVL BOOST simulation software, which was utilized for the engine and emissions modeling in this research. The support and assistance received from AVL under the University Partnership Program are greatly appreciated.

Conflict of interest

The authors declare that there is no conflict of interest.

Data availability statement

The data illustrated in the present study are available on a reasonable request from the authors.

Supplementary material

thumbnail Supplementary Fig. 1

Schematic representation of the experimental apparatus.

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All Tables

Table 1

Technical specifications of the research engine [24].

Table 2

Physicochemical properties of SOME and reference diesel fuel [24]

Table 3

CO production/destruction model [51].

Table 4

The NOx prediction model by Pattas and Hafner [51].

All Figures

thumbnail Fig. 1

Schematic model of test engine generated by AVL software (SB1: System input boundary condition; SB2: System output boundary condition; E1: Engine; C1: Cylinder; I1: Injector; CL1: Air cleaner; PL1, PL2: Plenum; R1, R2, R3: Restrictions; MP1, MP2, MP3, MP4, MP5, MP6, MP7, MP8: Measuring points).

In the text
thumbnail Fig. 2

Variation of BSFC for SOME and diesel fuel concerning the engine load and CR.

In the text
thumbnail Fig. 3

Difference in BSFC outcomes between experimental and simulation studies concerning ranging CR and engine loads.

In the text
thumbnail Fig. 4

Variation of CO emission for SOME and diesel fuel concerning the engine load and CR.

In the text
thumbnail Fig. 5

CO emission difference between experimental and simulation studies at ranging CR and engine loads.

In the text
thumbnail Fig. 6

Variation of CO2 emission for SOME and diesel fuel concerning the engine load and CR.

In the text
thumbnail Fig. 7

CO2 emission difference between experimental and simulation studies at ranging CR and engine loads.

In the text
thumbnail Fig. 8

Variation of HC emission for SOME and diesel fuel concerning the engine load and CR.

In the text
thumbnail Fig. 9

HC emission difference between experimental and simulation studies at ranging CR and engine loads.

In the text
thumbnail Fig. 10

Variation of NOx emission for SOME and diesel fuel concerning the engine load and CR.

In the text
thumbnail Fig. 11

NOx emission difference between experimental and simulation studies at ranging CR and engine loads.

In the text
thumbnail Supplementary Fig. 1

Schematic representation of the experimental apparatus.

In the text

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