Computational and Experimental Studies on the Behavior of Sprays with Different Orifice Characteristics and Fuel Properties of Biodiesel Crude Palm Oil (CPO) on a Premix Injector—A Comprehensive Review

Abstract

Large-scale industrial burners are essential components in various industries including power generation and chemical processing. Enhancing their energy efficiency and reducing emissions, particularly nitrogen oxides (NOx), requires a combination of experimental research and computational fluid dynamics (CFD) simulations. While there exist numerous emission control techniques, the main focus of the present review study was the passive control technique. The result of this review indicates that biodiesel fuel crude palm oil (CPO) was found to reduce emission components, particularly carbon components and particulate matter (PM). Moreover, it also mitigates cavitation within the injector’s orifice, reducing wear and tear. Although cavitation enhances spray atomization and creates finer droplets for improved combustion, it can damage injector orifices. Optimizing the orifice design, such as by adopting conical orifices over cylindrical ones, can significantly reduce cavitation and its adverse effects. Furthermore, innovations such as swirling fuel–air premixing within injectors enhance combustion efficiency and lower emissions by improving fuel–air mixing. However, spray characteristics, particularly the Sauter mean diameter (SMD), remain critical for predicting combustion performance. Further investigations into spray fineness and its impact on combustion dynamics are essential for advancing emission control and performance optimization.

1. Introduction

The global environmental effects have become prominent issues that draw the interest of researchers at a global level [1]. In order to face these new challenges in the coming years, efforts to reduce harmful gases by improving burner systems need to be prioritized [2,3]. Governments and organizations from almost all over the world are working hard to protect the environment by establishing stringent legislation and regulations for controlling emissions, particularly of nitrogen oxide (NOx) [4,5,6,7]. Reducing emissions is vital for combating climate change, improving air quality, and protecting public health. Greenhouse gases (GHGs) such as carbon dioxide (CO₂) and pollutants like nitrogen oxides (NOx) and particulate matter (PM) are major contributors to global warming and respiratory diseases. Transitioning to cleaner energy sources, including alternative fuels, is crucial for achieving sustainable energy goals. The innovative burner system features exceptional efficiency, utilizing a uniquely engineered oil burner to ensure highly effective combustion. This design significantly lowers oil consumption while curbing greenhouse gas emissions and other pollutants. Enhancing the energy efficiency of industrial-scale burners to achieve minimal emissions, particularly NOx, necessitates experimental studies complemented by computational fluid dynamics (CFD) simulations [8]. Various strategies have been created over the years to curb emissions. These methods of emission control are classified into two primary categories: active and passive control methods. Active control techniques modify flame characteristics through external influences on the combustion system such as acoustic forcing or magnetic fields. Conversely, passive control methods adjust flame dynamics by altering the initial conditions like fuel selection, varying fuel and oxidizer proportions, or changing burner geometry [9]. Thus, the primary objective of this review is to explore passive control techniques while examining the impacts of varying fuel properties and specific modifications made to the premix injector.

1.1. Biodiesel as an Alternative Fuel

The global rise in population and excessive fossil fuel consumption are significant concerns today. Technological advancements have resulted in an increased reliance on hydrocarbon fuels [10]. As a result, there is a pressing need to find alternative oils to either replace or lessen the reliance on hydrocarbon fuels [11]. The rise in oil prices and the need to minimize greenhouse gas emissions are driving global advancements in renewable energy sources. Energy security and environmental issues have been a concern for researchers, particularly in biologically derived alternative fuels. Biodiesel, a renewable fuel, is derived from esters of vegetable oils and animal fats [11,12]. Biodiesel offers a superior combustion emission profile, emitting lower levels of particulate matter, unburned hydrocarbons, and carbon monoxide (CO) than petroleum-based diesel. The carbon dioxide generated from burning biodiesel is recycled through photosynthesis, helping to mitigate its impact on the greenhouse effect [13,14,15]. Moreover, biodiesel also acts as a lucrative alternative fuel, which is particularly used in burner systems, compression ignition engines, and gas turbines. It is a renewable energy source that has similar properties to petroleum diesel fuel and can be used without the need for significant changes in the design of a burner [4]. It is basically a mixture of a few organic molecules with high molecular mass that has higher density and viscosity than petroleum diesel. However, the properties of evaporation and cavitation of biodiesel are quite different compared with those of diesel [5]. Nevertheless, its benefits in terms of fuel economy and environmental impact are well acknowledged [5]. Biodiesel can also be produced from various biomasses, while most of it is produced through the process of transesterification. The usage of biodiesel also contributes to the reduction in unwanted emissions such as particulate matter (PM) and unburned hydrocarbons [4,5]. However, it increases NOx emissions, which is still an issue for researchers in the field [5]. This is because the NOx produced from the combustion causes many unfavorable environmental influences including the ozone hole, photochemical smog, and acid rain [6].

1.2. Spray Combustion and Its Key Contribution

Understanding the fundamental phenomena of spray formation and atomization is required to achieve better performance and emissions reduction and to prolong the lifetime of the furnace and burner [16]. Sprays are created when the interface between a liquid and a gas deforms, resulting in the formation of liquid droplets [17]. They shift out and then enter the body of the gas, as the gas has a negligible impact on the dynamics and kinematics of the droplets’ formation process. There are a few spray characteristics for combustion performance that are always investigated, such as mean droplet size, droplet size distribution, patternation, cone angle, and penetration. The mean droplet size, droplet size distribution, and patternation mostly depend on the design of the atomizer used. Meanwhile, the cone angle and penetration are partially dependent on the atomizer design and on the aerodynamic influences [18,19].

Spray combustion is commonly used in the process of industries for the purpose of gaining energy and power. Nowadays, the popularity of effervescent atomizers for combustion purposes is increasing. Lefebvre and his colleagues were the first researchers to introduce those atomizers in the late 1980s [16]. However, spray formation and atomization processes are greatly dependent on the nozzle design [4,5]. This is because the nozzle geometry of a burner plays a significant role in flow characteristics, atomization and formation of the fuel–air mixture while improving combustion performance and reducing pollutant products [2,20,21].

Numerous analyses and investigations pertaining to combustion and emissions have been performed in the burner system experimentally and numerically all over the world. However, the current extensive literature review found that detailed studies on the characteristics of spray atomization of nozzle geometry in a burner are less frequent. Only a few studies are available concerning the spray characteristics of biodiesel fuel. Moreover, numerical simulation studies available on the influences of nozzle characteristics on spray behavior in the burner are relatively few compared with studies of the internal combustion engine.

Ing et al. [18] performed a numerical study on the spray characteristics of a blend of biofuels with diesel and commercial diesel fuel (CDF) in a gas turbine. It was highlighted that the blends of biodiesel with diesel have larger estimated diameters compared with diesel fuel. This occurs because the fuel properties influence spray formation. Moreover, the blends’ higher viscosity compared with diesel fuel resulted in unstable sprays and droplets. Furthermore, they also predicted that when the fuel viscosity increases, the Sauter mean diameter (SMD) also increases. Therefore, diesel fuel produces the smallest SMD than other blends of biodiesel. Similarly, Suh et al. [22], performed an experimental analysis examining the flow characteristics within a scaled-up nozzle for petrodiesel and biodiesel. Their findings indicated that biodiesel had a lower injection velocity and mass flow rate compared with petrodiesel, due to its higher viscosity.

Benajes et al. [23] studied experimentally how conical and cylindrical nozzle orifices impact injection rate behavior under steady-state conditions using a cavitation test rig. The study revealed that a conical orifice reduces cavitation and improves flow efficiency (discharge coefficient) and exit velocity in comparison with a cylindrical orifice. Payri et al. [24] investigated the impact of cavitation on diesel spray behavior, noting that the mass flow rate of a conical nozzle correlated with the pressure drop, provided there was no cavitation at the nozzle exit. Meanwhile, the cylindrical nozzle was in smothery condition. Furthermore, the injection velocity for the cylindrical nozzle rose due to the existence of the vapor at the orifice exit. However, Han et al. [25] noted that nozzle geometry significantly affects the primary breakup region when comparing conical and cylindrical nozzles.

Som et al. [26] conducted both numerical and experimental studies on the impact of the hole and hydrogrinding on sprays. It was found that conical nozzle shapes significantly decrease cavitation and turbulence levels within the orifice. Additionally, they pointed out that conical shapes could delay the primary breakup process, resulting in larger spray droplets, increased penetration, and a wider cone angle. Moreover, Som et al. [4] also performed a numerical study to quantify the differences between the biodiesel and petrodiesel flows through the inner nozzle flow dynamics. It was observed that biodiesel was less cavitating than petrodiesel. Biodiesel’s higher viscosity led to decreased flow efficiency and injection velocity. Additionally, the turbulence levels at the nozzle’s orifice exit were lower for biodiesel. However, it was noticed that the spray penetration was marginally higher for biodiesel compared with petrodiesel. The cone angle also was relatively lower. It could be concluded that these criteria were attributed to its poor atomization characteristics.

Similarly, Battistoni et al. [5] conducted simulations comparing diesel and biodiesel. They found that both fuels exhibited similar outlet values for mass flow rate, velocity, turbulent kinetic energy, and volume fraction when using a cylindrical (cavitating) hole. However, the diesel performed slightly better with a conical nozzle. The concurrent phenomena of higher density leading to higher mass flow was seen as an advantage for biodiesel. Moreover, the lower viscosity caused an increase in the discharge coefficient and exit velocity at the nozzle outlet, which is an advantage for diesel. Furthermore, the conical nozzle can achieve higher flow rates since cavitation is strongly reduced. However, with respect to spray, the biodiesel spray resulted in less sensitivity to the hole shaping. SMD was predicted to be larger for biodiesel, irrespective of hole shape.

1.3. Advanced Application Techniques in the Burner System for Emission Reduction

Nitrogen oxides (NOx) contribute to significant environmental problems, like ozone depletion, acid rain, and photochemical smog, raising global concerns. Environmental protection agencies and governments worldwide have enforced strict regulations to control these emissions. Effective combustion modeling developed over the past two decades [27] helps manage emissions from burner combustion. Currently, numerous technologies, such as low-NOx burners, overfire airflow (OFA), swirl burners, selective non-catalytic reduction, and selective catalytic reduction, have been developed to mitigate emissions [6].

Huang et al. [28] investigated how OFA impacts airflow and coal combustion in a 670 t/h wall-fired boiler to determine the optimal OFA settings and configurations. They found that NOx concentrations at the furnace outlet decreased. Zeng et al. [29] explored the combustion traits and NOx emissions of a 300 MWe utility boiler, discovering a nonlinear link between NOx emissions and the angles of the outer secondary-air vanes. Moreover, Zhou et al. [6] conducted numerical studies to optimize a single low-NOx swirl burner, examining how the size and structure of the swirl burner’s primary air pipe influenced flow, combustion, and NOx formation. Their findings revealed a 39.8% reduction in NOx emissions.

However, examining the interaction of turbulent combustion and its effects, including selecting the most suitable turbulence model, depends on the specific application. Typically, burner orifice modeling is not included in this analysis [27]. The geometrical burner is a crucial method for controlling emissions, significantly enhancing spray combustion, atomization, and fuel–air mixture formation. It also boosts combustion efficiency and reduces pollutants from the burner system [2]. Some researchers have concluded that burner geometry plays a key role in improving combustion and reducing emissions. Miller [30] examined how fuel quality impacts burner design and ignition stability, focusing on reducing NOx emissions. The studies highlighted that burner orifice geometry significantly affects combustion performance and emission reduction. Nevertheless, additional research on the premix swirl burner orifice could be beneficial for further minimizing emissions.

2. Biodiesel Crude Palm Oil (CPO) as an Alternative Fuel

2.1. CPO

CPO is the unrefined and unpurified oil obtained from palms that is extracted from the palm nut kernel. It is a natural oil that contains higher levels of saturated fat compared with other natural oils like vegetable and olive oils, which are rich in unsaturated fats. CPO has non-glyceride components such as kernel shell pieces, trace metals, and products of oxidation in its crude oil state. Therefore, it has to undergo a purification process to remove these components in order to obtain the edible and sellable palm oil. Moreover, the composition of CPO comprises useful constituents with high concentrations such as tocopherols, carotenoids, tocotrienols, phospholipids, sterols, triterpene alcohols, aliphatic hydrocarbons, squalene, and aliphatic alcohols [31,32]. CPO plays an important role in the economy as its value is as an edible ingredient for refined palm oil. The global demand for CPO is rising because its annual production is currently limited [31].

2.2. Properties of CPO

Biodiesel derived from CPO exhibits several key physical properties critical to its evaluation as a sustainable fuel source. The density of CPO-based biodiesel typically falls between 860 and 900 kg/m³, aligning with the specifications set by the ASTM standard. Its kinematic viscosity at 40 °C ranges from 3.5 to 5.0 mm²/s, ensuring smooth flow properties in fuel systems. The cetane number, a measure of combustion quality, usually exceeds 50, making it suitable for diesel engines. The flash point, a critical safety indicator, often exceeds 120 °C, demonstrating its low volatility and safe handling characteristics. Furthermore, biodiesel from CPO has a high oxidative stability, attributed to its content of saturated and unsaturated fatty acids such as methyl hexadecanoate and methyl 9-octadecenoate. These fatty acid profiles enhance its suitability for storage and use in various climatic conditions [33]. Studies have shown that esterification followed by transesterification significantly reduces free fatty acid levels in CPO, enhancing biodiesel yield and quality. Advances in catalysis, such as using potassium oxide (K₂O), have improved reaction efficiency and yield, with up to 73.2% conversion observed under optimized conditions [34,35].

Table 1. The physical characteristics of CPO, RBD, and WCO and the ASTM standard [36].

Properties	ASTM Standard	CPO	RBD	WCO
Acid value (mg KOH/g)	D 974	-	-	18.21
Flash point (°C)	D3828	214	266
Cloud point (°C)	D97	43	15
Pour point (°C)	D97	14	7
Density at 27 °C (kg/L)	D1217	0.9042–65	0.885	0.8989
Calorific value (kJ/kg)	D976	39,285	39,357	39,198
Kinematic viscosity at 40 °C (cSt)	D445	60	42	46.5
Color	Visual	Orange	Light yellow	Reddish brown
Sulfur content	IP242	-	0.0325	-

demonstrates the characteristics of CPO; refined, bleached, and deodorized (RBD) palm oil; and waste cooking oil (WCO). According to the calorific values, it shows that these three basic oils have potential as the raw materials for the production of biodiesel. The flash point of the CPO is higher than 100 °C, which means that the CPO is safe for storage at ambient temperatures. The higher flash point decreases the risk of fire and is one of the advantages of the CPO. The kinematic viscosity of CPO is much higher than that of the standard biodiesel. Thus, it should be alleviated strongly to meet the standards of biodiesel in the transesterification process. In addition, high viscosity has a significant effect on the spray injection from the injector and causes incomplete combustion and carbon deposition as well [36].

2.3. Palm Oil Biodiesel (POB)

Benjumea et al. [37] used basic metanalysis of CPO to produce biodiesel with a methanol-to-oil molar ratio of 12:1 and 0.6% sodium hydroxide as the catalyst. The palm oil biodiesel (POB) methyl ester composition was analyzed using gas chromatography with the reaction occurring at 60 °C for one hour. Normally, several blends with wide composition boundaries are prepared by splash blending [38]. However, B5 (5% POB-95% diesel by volume) and B20 blends are the most common working fluids used in the test. Blends are made on a volume basis at 25 °C, with pure fuels referred to as B100 and B0.

Furthermore, Benjumea et al. [37] experimentally demonstrated that palm oil biodiesel (POB) has a higher cloud point, attributed to its long hydrocarbon chains. At approximately 16 °C, POB begins to crystallize, forming solid bio-wax that can thicken the oil and obstruct fuel filters and injectors.

2.4. Investigations into Crude Palm Oil (CPO)

Nowadays, blends of biodiesel and diesel have been used widely by researchers from all over the world in an effort to replace the diesel with an alternative fuel. A great number of researchers have used biodiesel CPO blended with diesel as the working fuel in burner combustion, rapid compression machines, and internal combustion engines to determine the combustion performance and emission. Leevijit et al. [39] carried out an experimental study to examine and compare the performance and emissions of an indirect injection (IDI) turbo diesel engine for automobiles. Blends of diesel and Dg-aMCPO were used with selected portions of 20, 30, and 40 vol.%. The experiment, conducted over a short period with varying loads and speeds, revealed that all fuel blends produced identical maximum brake torque and power. The higher blend ratio (40 vol.%) led to a marginally higher BSFC and slightly lower BTE, EGT, and black smoke emissions. Furthermore, NOx emissions were slightly elevated across all blends, while the 20 vol.% blend exhibited significantly reduced CO emissions [39].

Phoungthong et al. [40] also performed an experiment that focused on the examination of emissions from agricultural diesel engines with blends of degummed–deacidified mixed crude palm oil (MCPO) such as PB30 and PB40. They were mixed with 30% and 40% by volume of CPO. The result obtained was comparable to the commercial diesel. Figure 1 shows the particle size distribution of the PM and PAHs with the fuels PB0, PB30, and PB40 [40]. It was observed that most of the PM size distributions were found in the <1 μm diameter range. It was predicted that the combustion of PB30 and PB40 resulted in smaller particle sizes than PB0, as the mass median aerodynamic diameters (MMADs) decreased with higher palm oil blending percentages from PB0 to PB40. Furthermore, the increase in particle size caused a reduction in total particle-bound PAHs, which were similar to the PM concentrations.

Furthermore, Khalid et al. [41] examined how preheating biodiesel influences the fuel properties, spray behavior, and mixture formation through direct photography. The study found that preheated fuel improved spray penetration and created a larger spray area and better fuel–air premixing, as illustrated in Figure 2. Khalid et al. [20] also examined how the storage duration of different CPO biodiesel blends at varying storage temperatures impact the fuel properties and burner exhaust emissions. They concluded that storage duration and temperature influence biodiesel density, viscosity, and carbon monoxide emissions. Higher blending ratios led to variations in emissions due to the increased oxygen content of the fuel, as depicted in Figure 3.

Khalid et al. [42] conducted an experimental study using blends of CPO biodiesel of B5, B10, and B15 and diesel fuel to assess the mixing injector fuel and water–fuel emulsion for an open burner. They analyzed the mixture spray formation behavior during fuel–air premixing. The study found that CPO biodiesel exhibited longer penetration length and spray area compared with diesel, but with a smaller spray angle. Additionally, they observed that the water–fuel mix produced a brighter and shorter flame than the pure fuel. Jaafar et al. [36] characterized the spray of refined, bleached, and deodorized palm oil (RBDPO) and diesel blends using a phase Doppler particle analyzer. Their results showed that as the percentage of RBDPO in the blend increased, the SMD also increased, while the spray angle decreased, as illustrated in Figure 4.

Likewise, Khalid et al. [42] studied the impacts of emulsified biodiesel palm oil blends (B5, B10, and B15) on mixture formation, combustion, and flame development within the burner system. They incorporated water content into the combustion process through biodiesel–water emulsification, with equivalent ratios ranging from 0.6 to 2.0. Their findings indicated that mixture formation influences both the ignition process and the flame development. Additionally, higher water content significantly alters the chemical processes during fuel–air mixing, affecting flame development and the combustion process.

3. Background of CFD Simulation

CFD involves simulating fluid behavior in engineering systems through physical problem formulation, modeling, and numerical techniques [44]. This simulation method offers the advantage of accurately replicating complex scenarios such as transonic or turbulent flows while requiring minimal time, budget, and human resources.

3.1. Governing Equations [45]

The Navier–Stokes equations, as shown below in Equations (1)–(5), are the governing equations that are undertaken for fluid dynamic problems.

(i)

Continuity equation:

$$\mathrm{Gas}\mathrm{phase}{\displaystyle \frac{\partial }{\partial t}}\left({\epsilon }_g{\rho }_g\right)+\nabla .\left({\epsilon }_g{\rho }_g\overrightarrow{V}\right)=0$$

(1)

$$\mathrm{Solid}\mathrm{phase}{\displaystyle \frac{\partial }{\partial t}}\left({\epsilon }_p{\rho }_p\right)+\nabla .\left({\epsilon }_p{\rho }_p\overrightarrow{V_p}\right)=0$$

(2)

(ii)

Momentum equation:

$$\mathrm{Gas}\mathrm{phase}{\displaystyle \frac{\partial }{\partial t}}\left({\epsilon }_g{\rho }_g\overrightarrow{V_g}\right)+\nabla .\left({\epsilon }_g{\rho }_g\overrightarrow{V_g}\overrightarrow{V_g}\right)=\nabla .\stackrel{=}{{\tau }_g}{\epsilon }_g{\rho }_{g}g-I_{gp}$$

(3)

$$\mathrm{Solid}\mathrm{phase}{\displaystyle \frac{\partial }{\partial t}}\left({\epsilon }_p{\rho }_p\overrightarrow{V_p}\right)+\nabla .\left({\epsilon }_p{\rho }_p\overrightarrow{V_p}\overrightarrow{V_p}\right)=\nabla .\stackrel{=}{{\tau }_p}{\epsilon }_p{\rho }_{p}g-I_{gp}$$

(4)

(iii)

Energy equation:

$$\rho c_{\mu }{\displaystyle \frac{\partial T}{\partial t}}+\rho c_{\mu }{U}_i{\displaystyle \frac{\partial T}{\partial x_i}}=-P{\displaystyle \frac{\partial U_i}{\partial x_i}}+\lambda {\displaystyle \frac{{\partial }^{2}T}{\partial {x^2}_i}}+{\tau }_{ij}{\displaystyle \frac{\partial Uj}{\partial x_i}}$$

(5)

3.2. The Turbulence Models Equations [45]

In CFD simulations, turbulence models typically fall into three categories: RANS, LES, and DNS models. RANS and LES models are commonly utilized in various simulations, with RANS being a primary tool for burner combustion chamber design. Recently, LES has seen substantial progress and is increasingly influential in the design process [46]. The LES model is typically used for transient turbulence models, while the RANS model solves few turbulence models with a specific number of equations involved in the model. The Spalart–Allmaras model is categorized as a one-equation turbulence model. Models like standard k–ε, RNG k–ε, realizable k–ε, standard k–ω, and SST k–ω are considered two-equation turbulence models. The V2F model falls under the four-equation category. Steady turbulence RANS models include the Reynolds stress model, the k–kl–ω Transition Model, and the SST Transition Model. The turbulence models usually used are the k-ε turbulence model the k–ω turbulence model, as shown in Equations (6)–(14).

(i)

Standard k–ε turbulence model [47]:

$${\displaystyle \frac{\partial \left(\rho k\right)}{\partial t}}+{\displaystyle \frac{\partial \left(\rho k{u}_i\right)}{\partial x_i}}={\displaystyle \frac{\partial }{\partial x_j}}\left[{\displaystyle \frac{{\mu }_i\partial k}{{\sigma }_k\partial x_j}}\right]+2{\mu }_t{E}_{ij}{E}_{ij}-\rho \epsilon$$

(6)

$${\displaystyle \frac{\partial \left(\rho \epsilon \right)}{\partial t}}+{\displaystyle \frac{\partial \left(\rho \epsilon u_i\right)}{\partial x_i}}={\displaystyle \frac{\partial }{\partial x_j}}\left[{\displaystyle \frac{{\mu }_i\partial \epsilon }{{\sigma }_{\epsilon }\partial x_j}}\right]+C_{1\epsilon }2{\mu }_t{E}_{ij}{E}_{ij}-C_{2\epsilon }\rho {\displaystyle \frac{{\epsilon }^2}{k}}$$

(7)

(ii)

RNG k–ε turbulence model [48]:

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho k\right)+{\displaystyle \frac{\partial }{\partial x_i}}\left(\rho k{u}_i\right)={\displaystyle \frac{\partial }{\partial x_j}}\left(\alpha k{\mu }_{eff}{\displaystyle \frac{\partial k}{\partial x_j}}\right)+G_k+G_b-\rho \epsilon -Y_M+S_k$$

(8)

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho \epsilon \right)+{\displaystyle \frac{\partial }{\partial x_i}}\left(\rho \epsilon u_i\right)={\displaystyle \frac{\partial }{\partial x_j}}\left(\alpha \epsilon {\mu }_{eff}{\displaystyle \frac{\partial \epsilon }{\partial x_j}}\right)+C_{1\epsilon }{\displaystyle \frac{\epsilon }{k}}+\left(G_k+C_{3\epsilon }{G}_b\right)-C_{2\epsilon }\rho {\displaystyle \frac{{\epsilon }^2}{k}}-R_{\epsilon }+S_{\epsilon }$$

(9)

(iii)

Realizable k–ε turbulence model [49]:

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho k\right)+{\displaystyle \frac{\partial }{\partial x_j}}\left(\rho k{u}_j\right)={\displaystyle \frac{\partial }{\partial x_j}}\left[\left(\mu +{\displaystyle \frac{{\mu }_t}{{\sigma }_k}}\right){\displaystyle \frac{\partial \epsilon }{\partial x_j}}\right]+G_k+G_b-\rho \epsilon -Y_M+S_k$$

(10)

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho \epsilon \right)+{\displaystyle \frac{\partial }{\partial x_j}}\left(\rho \epsilon u_j\right)={\displaystyle \frac{\partial }{\partial x_j}}\left[\left(\mu +{\displaystyle \frac{{\mu }_t}{{\sigma }_{\epsilon }}}\right){\displaystyle \frac{\partial \epsilon }{\partial x_j}}\right]+\rho C_{1}S\epsilon -\rho C_2{\displaystyle \frac{{\epsilon }^2}{k+\sqrt{v\epsilon }}}+C_{1\epsilon }{\displaystyle \frac{\epsilon }{k}}{C}_{3\epsilon }{G}_b+S_{\epsilon }$$

(11)

$$C_1=max\left[0.43,{\displaystyle \frac{\eta }{\eta +5}}\right], \eta =S{\displaystyle \frac{k}{\epsilon }}, S=\sqrt{2S_{ij}{S}_{ij}}$$

(iv)

k–ω two-equation turbulence model [50]:

$${\displaystyle \frac{\partial \left(\rho k\right)}{\partial t}}+{\displaystyle \frac{\partial \left(\rho k{u}_j\right)}{\partial x_i}}=P-{\beta }^{*}\rho \omega k+{\displaystyle \frac{\partial }{\partial x_j}}\left[\left(\mu +{\sigma }_k{\displaystyle \frac{\rho k}{\omega }}\right){\displaystyle \frac{\partial k}{\partial x_j}}\right]$$

(12)

$${\displaystyle \frac{\partial \left(\rho \omega \right)}{\partial t}}+{\displaystyle \frac{\partial \left(\rho u_j\omega \right)}{\partial x_j}}={\displaystyle \frac{\gamma \omega }{k}}P-\beta \rho {\omega }^2+{\displaystyle \frac{\partial }{\partial x_j}}\left[\left(\mu +{\sigma }_{\omega }{\displaystyle \frac{\rho k}{\omega }}\right){\displaystyle \frac{\partial \omega }{\partial x_j}}\right]+{\displaystyle \frac{\rho {\sigma }_d}{\omega }}{\displaystyle \frac{\partial k}{\partial x_j}}{\displaystyle \frac{\partial \omega }{\partial x_j}}$$

(13)

(v)

SST transition turbulence model [51]:

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho k\right)+{\displaystyle \frac{\partial }{\partial x_j}}\left(\rho k{u}_j\right)={\tilde{P}}_k-{\tilde{D}}_k+{\displaystyle \frac{\partial }{\partial x_j}}\left(\left(\mu +{\sigma }_k{\mu }_t\right){\displaystyle \frac{\partial k}{\partial x_j}}\right)$$

(14)

$${\tilde{P}}_k={\gamma }_{eff}{P}_k$$

(15)

$${\tilde{D}}_k=min\left(max\left({\gamma }_{eff},0.1\right),1.0\right)D_k$$

(16)

$$R_y={\displaystyle \frac{\rho y\sqrt{k}}{\mu }}$$

(17)

$$F_3=e^{{-\left(\frac{R_y}{120}\right)}^3}$$

(18)

$$F_t=max\left(F_{1orig,}{F}_3\right)$$

(19)

3.3. Mesh Density

Mesh density typically has three levels: coarse, medium, and fine. Fine mesh represents the highest level of grid precision in mesh generation. The density of the mesh greatly influences the problem’s solution. In other words, the precision of the outcomes may largely depend on the mesh quality [44,45,52,53,54,55]. Sha et al. [55] concluded that the mesh quality, especially the grid spacing near the wall surface, is crucial for accurate heat flux computations. Zhang and Yu [52] performed a numerical simulation to develop a theoretical framework for designing grid sizes in small-scale structures generated in hypersonic flows, where both viscous and non-viscous flows interact. The results showed that the accuracy of the numerical simulation is influenced by the grid sizes. The best simulation results were obtained when the grid design of the small-scale structure of the interaction shear flows (ISF) theory was used. Similarly, Chao et al. [56] proposed that using a refined grid yields improved simulation results of the flow field.

Figure 5 shows a CFD model with different mesh densities that was performed on two different grids to determine the possibility of grid sensitivity. The models were meshed with the same tetragonal cell type. The primary distinction in mesh sensitivity was the cell density. As shown in Figure 5a, the densest grid comprises 216,440 cells with an average side dimension of 11.3 μm. In contrast, Figure 5b shows a less dense grid, with 30,920 cells and an average side dimension of 22.6 μm. The less dense grid was employed because the results for both grids were nearly identical [57].

4. Premix Injector

A premix injector is a device designed to mix fuel and air before combustion, ensuring a more uniform and efficient burn. It is commonly used in advanced combustion systems, such as gas turbines and lean-burn engines, to reduce emissions and enhance energy efficiency. By achieving thorough premixing of fuel and air, the injector helps minimize the formation of harmful pollutants like NOx while maintaining stable combustion. Premix injectors are critical components in applications requiring precise control of the air–fuel ratio for optimal performance and environmental compliance [58,59]. Yatsufusa et al. [60] developed a novel injector capable of directly injecting water into the combustion zone to form a water–fuel emulsion. This emulsified fuel effectively lowers the flame temperature, leading to a reduction in NOx and PM emissions [61,62,63,64,65]. The advanced injector is engineered to enable the rapid internal mixing of fuel and water. Within a compact mixing chamber, atomization air promotes the swift blending of fuel and water, which is subsequently injected into the chamber. This mixture of water, fuel, and air is then directly delivered to the combustion zone. The newly developed injector, termed the “fuel-water internally rapid mixing type injector”, independently introduces fuel and water into a small mixing chamber. Figure 6 provides a detailed cross-sectional view of the injector. The mixing chamber has a capacity of 3.9 mL, with fuel being supplied through a ring-shaped slit and water through a central hole. Additionally, the injector’s swirler generates swirl airflow within the mixing chamber to enhance mixing efficiency [60]. By incorporating this advanced mixing mechanism that resulted from the swirling features, it ensures a more uniform air–fuel mixture. This improves combustion efficiency, reduces emissions, and minimizes unburned hydrocarbons and particulate matter. Rapid-mixing injectors are particularly beneficial in applications where precise control of combustion dynamics is critical, such as in advanced diesel engines or gas turbines.

4.1. Fuel–Air Premixing

Fuel–air premixing refers to the process of thoroughly blending fuel and air before combustion occurs. This technique ensures a uniform mixture, leading to more efficient combustion and lower emissions. By achieving an even distribution of fuel and air, premixing helps reduce the formation of harmful pollutants such as NOx and PM [66,67]. Premixing is particularly critical in applications such as gas turbines, lean-burn engines, and industrial burners, where precise control of the air–fuel ratio is required for optimal performance and compliance with environmental regulations. Advanced premixing techniques often involve specialized injectors, turbulence-enhancing devices, or high-pressure fuel injections to improve mixture homogeneity and combustion efficiency [68]. Mixing occurs slowly and is not driven by forced convection. Turbulent mixing is crucial in practical fluid flows exceeding a millimeter in size. Ultra-low-emission combustors depend on achieving nearly perfect mixture homogeneity before successful combustion. A homogeneous combustible mixture significantly lowers the risk of auto-ignition. Achieving a high degree of mixture uniformity is crucial for both reducing NOx emissions and preventing auto-ignition [58]. Furthermore, effective turbulent flow mixing can be achieved by combining water, fuels, gases, and other substances. The formation of a fuel–air mixture is crucial for combustion modeling and is a complex process that can be executed in various ways using diverse technologies.

Over the past 20 years, numerous researchers have examined spray fuel–air mixing, a field pioneered by the eminent spray researcher Lefebvre [67]. Various types of atomizers were employed to mix fuel and air for both internal and external combustion applications. The atomizers developed were highly effective for fuel–air mixing, leading to their widespread production for spray injection applications. Furthermore, recent studies on fuel–air premixing have emphasized the role of advanced mixing techniques in enhancing combustion efficiency and minimizing emissions. High-pressure injection and the use of swirling flows and turbulence generators improve the uniformity of the mixture, leading to better flame stability and reduced pollutant formation. These methods contribute to lowering NOx and particulate matter emissions by controlling the air–fuel ratio more precisely [69,70]. Studies also show the importance of premixing in reducing thermal NOx formation, particularly in high-temperature environments.

4.2. Effects of Swirling Flow

Swirling flow in combustion systems enhances fuel–air mixing by creating vortexes, which improve the combustion efficiency and flame stability [71,72]. This leads to more complete combustion, reducing pollutants such as NOx and CO by lowering flame temperatures [73]. Swirling flow promotes better fuel–air homogenization, stabilizes flames, and increases the residence time of the mixture in the combustion zone [74,75]. These effects are crucial for improving combustion in industrial burners and gas turbines, where emission control and efficiency are critical [74,76].

For many years, researchers and industries have frequently utilized swirling flows to stabilize high-intensity combustion processes. Liu et al. [71] examined how the nozzle gap shroud impacts combustion stability and spray characteristics in a swirl-cup combustor. They looked at various performance aspects, including flame patterns, ignition, and lean blowout. Their findings showed a significant improvement in ignition performance when the fuel–air ratio was below 18% of the ignition limit. The interaction between swirling air and jet streams from the purge holes had a significant impact on the flow pattern. As the height (h) increased, the central recirculation zone moved downstream, and the reverse flow velocity was reduced, as shown in Figure 7.

Rahman et al. [77] conducted a numerical analysis of the performance of the separator that induces orthogonal flow to separate the water vapor two-phase flow. They used the standard and realizable k-ε turbulence models. It was found that the realizable k-ε exhibited a stronger swirling flow at the center and a weak swirling flow close to the wall, as shown in Figure 8. This is due to the centrifugal force that pushed the liquid to the outer vortex region, where it accumulated in the area of weak swirling intensity. Huang and Yang [78] considered LES techniques to numerically analyze the effect of inlet swirling on the swirl-stabilized combustor. The findings indicated that increasing the swirl number moved the recirculation zone upstream, merging it with the wake recirculation zone. It was observed that higher swirl numbers intensified turbulence, which reduced the flame speed and surface area. However, excessive swirling could lead to the central recirculating flow extending into the inlet annulus, potentially causing flame flashback.

Furthermore, Reis et al. [27] used the standard k-ε, RNG k-ε, the realizable k-ε, and the Reynolds stress models to compare the flow performances of an industrial burner orifice through CFD simulation. Similarly, Eiamsa-ard et al. [79] compared the standard k-ε turbulence model with the Reynolds stress model (RSM) for a flow through a circular orifice. The simulation results were consistent with the experimental findings. Nevertheless, the RSM model proved to be more precise in the downstream orifice region compared with the k-ε model. Coughtrie et al. [80] conducted a numerical study of the flow in a gas-lift digester with a central draft tube, employing various turbulence closure models, including the k-ω Shear-Stress-Transport (SST), Renormalization Group (RNG) k-ε, Linear RSM, and Transition SST models. Their findings indicated that the Transition SST model was the most effective at capturing mixing behavior and accurately predicting separation. Therefore, they recommended using the Transition SST model over k-ε models for similar mixing challenges.

Likewise, German and Mahmud [81] studied numerically the performance of different turbulence models using Reynolds stress turbulence models, RSM, and standard k-ε turbulence models in predicting turbulent generation for a non-premixed combusting swirling flow. It was observed that both the k-ε and Reynolds stress turbulence models produced fairly accurate predictions, especially concerning flame properties. However, the k-ε model was unable to replicate the subcritical nature of the isothermal flow, whereas the Reynolds stress model provided a superior prediction of flame characteristics. Additionally, the Reynolds stress model yielded better results for the gas temperature and oxygen concentration levels in the internal recirculation zone and surrounding the shear region.

4.3. Cavitation

Cavitation in combustion refers to the formation of vapor bubbles within a liquid fuel under high pressure and temperature conditions, typically near the injector nozzles or in the combustion chamber, as shown in Figure 9. This phenomenon can significantly impact combustion performance, fuel atomization, and the stability of the flame. Cavitation occurs when local pressure drops below the vapor pressure of the fuel, causing the fuel to vaporize and form bubbles. These bubbles collapse when the pressure increases, releasing energy that can lead to uneven fuel distribution, erosion of components, and potential instability in the combustion processes [82]. In fuel injectors, cavitation can result in irregular spray patterns, which negatively affect the efficiency and completeness of fuel combustion. Inconsistent atomization can lead to poor mixing of the fuel and air, increasing the formation of pollutants such as unburned hydrocarbons and particulate matter. Additionally, cavitation-induced turbulence can distort the combustion process, disrupting flame stability and combustion efficiency [83,84]. On the other hand, controlled cavitation effects are sometimes exploited in advanced combustion technologies to enhance fuel–air mixing in certain applications, such as in engines designed for lower emissions and higher efficiency. Moreover, cavitation also affects the structural integrity of the combustion components. The rapid collapse of vapor bubbles generates shockwaves that can lead to mechanical erosion of injector nozzles and combustion chamber walls. Over time, this erosion can cause wear and tear, potentially shortening the lifespan of critical engine components and requiring more frequent maintenance [85]. For this reason, understanding and mitigating cavitation in combustion systems is essential to improving both the operational efficiency and longevity of modern combustion engines.

Over the years, significant efforts to manage cavitation were exerted that focus on optimizing injector nozzle design, operating conditions, and fuel properties. It was indicated that increasing the temperature or altering the fuel’s viscosity can shift the conditions under which cavitation occurs, helping to prevent or reduce its negative effects [86]. Moreover, advanced computational fluid dynamics (CFD) simulations and experimental techniques were employed to predict cavitation behavior and design more efficient combustion systems [87]. Thus, the interaction between cavitation and swirling flows has also been a subject of study, as the swirling motion can either mitigate or exacerbate cavitation, depending on the flow conditions. Swirling flows tend to increase turbulence and promote better mixing of the fuel and air, but they can also lead to localized pressure drops that favor cavitation. Understanding this interplay is crucial for optimizing combustion performance while minimizing cavitation-induced damage [88]. Therefore, while cavitation in combustion systems can have detrimental effects on combustion efficiency and engine components, advancements in nozzle design, fuel characteristics, and computational modeling are improving the ability to predict and control this phenomenon. By carefully managing cavitation, it is possible to enhance fuel atomization, optimize combustion efficiency, and extend the lifespan of combustion systems.

Figure 9. Cavitation in a nozzle [89].

Figure 9. Cavitation in a nozzle [89].

5. Orifice Geometries of Injector

5.1. Orifice Diameter

The design of the nozzle hole significantly affects nozzle orifice flow, spray characteristics, cavitation, and turbulence levels, all of which have an indirect influence on combustion [4,26,30,90]. Researchers commonly use various types of nozzle orifices with different hole shapes (such as cylindrical or conical), and orifice’s diameter. Yang et al. [91] discovered that nozzle diameter influences atomization behavior when they compared the spray properties of two different nozzle diameters. Both injectors had the same swirl chamber size and inlet. Under identical pressure drops, the spray angle for the 1.5 mm nozzle was smaller than that for the 2 mm nozzle. The 1.5 mm nozzle produced a rotating fluid jet, while the 2 mm nozzle created a twisted ribbon-like structure due to stronger swirl effects.

Daikoku and Furudate [92] investigated how cavitation generation and collapse in a nozzle impact liquid breakup using three different nozzle length-to-diameter ratios. Their findings indicate that disturbances within the nozzle dissipate when flowing through a nozzle with a larger length-to-diameter ratio, which does not enhance liquid jet atomization. Conversely, in a smaller length-to-diameter ratio, cavitation occurs at low injection pressures, is unaffected by downstream flow, and promotes liquid jet breakup. This cavitation affects all transitions in a smaller nozzle ratio, reducing flow velocity and transitioning from a wavy jet to a spray. The liquid velocity against L/D ratio for the nozzles with diameters of 3 mm and 1.5 mm resulted in slower velocities with smaller nozzle diameters, as depicted in Figure 10.

The study also determined that increasing injection pressure leads to a larger spray angle, as illustrated in Figure 11. This finding is consistent with experimental results from [93,94]. Similarly, Sun et al. [95] conducted a numerical study on how the geometrical parameters of the nozzle impact flow and cavitation characteristics within an injector’s nozzle. They found that the ratio of length to diameter of the nozzle significantly affects these characteristics. Narrowing the orifice diameter increases drag force, sharply reducing flow velocity within the nozzle. Their study concluded that decreasing the nozzle diameter, given a specific length, leads to variations in the distribution of physical fields. Different nozzle diameters cause low (or even negative) pressure at various locations, resulting in distinct cavitation origins.

5.2. Nozzle Hole Shape

The nozzle hole shape significantly impacts fuel atomization and combustion efficiency. Cylindrical nozzles tend to produce a more uniform spray, while conical nozzles offer a wider spray angle, promoting better fuel–air mixing. The design of the nozzle, which is usually shaped and sized according to the specific needs of the combustion system, directly influences the performance and emission characteristics of the engine or burner [96,97]. Optimal nozzle design enhances fuel efficiency and reduces emissions, contributing to more sustainable combustion processes. Numerous studies, such as [23,24,25,98], have utilized cylindrical conical nozzle shapes to investigate their performance differences in cavitation, flow efficiency (discharge coefficient), exit velocity, spray characteristics, mass flow rate, etc.

Benajes et al. [23] conducted an experimental study to analyze how conical and cylindrical nozzle orifices affect injection rate behaviors in a cavitation test. The experiment compared these two orifice shapes to enhance flow efficiency, reduce cavitation, and increase exit velocity. It was noted that the fuel injection rate decreased due to the smaller exit area. Payri et al. [24] noted smooth conditions with cylindrical nozzles. Meanwhile, the mass flow rate for the conical nozzle correlated with the square root of the pressure drop when cavitation was absent at the nozzle exit. They also observed an increase in injection velocity due to vapor presence at the orifice outlet of the cylindrical nozzle. Additionally, Han et al. [25], when comparing conical and cylindrical nozzles, determined that nozzle geometry significantly influences the primary breakup region.

5.3. Conical Nozzle

A conical nozzle features a cone shape with a half-cone angle when viewed from the side [5,26,57,89,95]. The conicity or the coefficient of an orifice is defined as the ratio of the difference between the nozzle’s inlet diameter and outlet diameter to a constant 10 µm [95,99]. When the orifice coefficient is positive, the nozzle forms a conical pattern. Conversely, a negative coefficient value results in an inverted conical pattern. Conical nozzles offer a few advantages, such as their simple design and the lack of flow disruptions when propellants are ejected from the combustion chamber. This results in propellants exiting in a straight path from the throat to the exit, making conical nozzles particularly suitable for solid and hybrid propellant types. However, conical nozzles also have drawbacks, notably, significant divergence losses at the exit. As the propellant flows parallel to the centerline, rather than at the cone angle, energy is lost due to divergence, leading to lower nozzle efficiency. The large exit angle further increases divergence loss. Additionally, conical nozzles are heavier because they require more materials compared with other nozzle designs.

A pintle-type nozzle produces cone-shaped sprays and creates a homogeneous mixture to achieve low exhaust emissions during premixed diesel combustion. Unlike a nozzle jet burner, the conical nozzle shape effectively controls partial premixing levels and stabilizes partially premixed flames [100]. A lean, highly homogeneous mixture can be achieved using a conical spray injector in advanced diesel injection. Additionally, directing the fuel jets onto the guide wall just before the nozzle exit enhances the conical spray nozzle, creating a conical spray shape [90]. Furthermore, the conical nozzle offers higher performance characteristics, helping to suppress cavitation.

Jia et al. [90] performed computational simulations examining cavitations within conical spray nozzles. Their findings showed that cavitations affect the film thickness, fuel velocity at the nozzle’s exit, and spray angle. Pougatch et al. [101] conducted a numerical study on how conical nozzle attachments influence the distribution of the spray in a fluidized bed. It was discovered that smaller attachment angles stabilize the spray, whereas larger angles lead to destabilization. Moreover, Battistoni et al. [5] investigated cavitation while comparing cylindrical and conical nozzles. They found that fuel type had a minimal impact on cavitation regions, while nozzle shape was crucial. Cylindrical nozzles produced higher cavitating flow, whereas conical nozzles tended to reduce cavitation. However, diesel fuel showed greater liquid penetration in conical convergent nozzles. Elbaz et al. [100] explored the stabilization mechanisms, limits of stability, and flow field structures of highly stabilized, partially premixed methane flames in a conical nozzle burner with a concentric air co-flow. They found that introducing air co-flow enhanced stability, and as the air co-flow velocity increased, most of the stable flames became nearly fully premixed.

5.4. Cylindrical Nozzle

The cylindrical nozzle features a cylindrically shaped head at the exit, as depicted in Figure 12. Compared with conical nozzles, cylindrical nozzles are more prone to cavitation due to their smaller conicity and lower rounding radii values [98].

The flow rate of a nozzle rises in proportion to the square root of the pressure differential until it reaches stability, a condition caused by cavitation. This critical cavitation point, defined as Kcrit, marks where the mass flow rate begins to stabilize. As a cylindrical nozzle is prone to cavitation, this phenomenon is observed when mass flow rate stabilization happens at a specific injection pressure, leading to reduced discharge pressure (choking). Cavitation ensues if the cavitation number under these pressure conditions falls below the critical value, Kcrit [103].

Payri et al. [103] investigated the effects of cavitation on internal flow and the macroscopic spray behavior in diesel injection nozzles, comparing cylindrical and conical types. They observed an exception for the cylindrical nozzle, which exhibited a reduction in the discharge coefficient at each injection pressure with a notable influence of the Reynolds number, as shown in Figure 13. This drop in the discharge coefficient was linked to a reduction in the mass flow rate due to cavitation. Additionally, the increase in the discharge coefficient halted upon the onset of cavitation, indicating that the discharge coefficient is dependent on the cavitation number at that point.

5.5. Influences of Hole-Shaped Nozzles

In research on fuel properties and spray characteristics, cylindrical and conical hole nozzles are frequently selected. Since biodiesel exhibits a slightly higher mass flow in cavitating nozzles, using hole-shaped nozzles with similar or slightly higher mass flow can help reduce diesel cavitation. Som et al. [26] conducted numerical and experimental studies on how the hole shape and hydrogenization affect biodiesel sprays. They discovered that conicity notably reduced cavitation and turbulence levels within the nozzle orifice. Additionally, conicity was found to slow down the primary breakup process, resulting in larger spray droplets, greater penetration, and a smaller cone angle.

Hountalas et al. [104] conducted an experimental study of three different nozzle hole types to assess the impact of conical hole nozzles on engine performance and emissions. The results showed that the divergent nozzle hole produced more soot, but lower NOx emissions, compared with the other two nozzle types.

6. Spray Characteristics

Spray nozzles operate under different conditions based on their design. When selecting an appropriate nozzle, key spray characteristics such as spray pattern, capacity, impact, angle, and droplet size must be considered. These precision components are engineered to achieve specific spray characteristics under defined conditions. Factors like seat angle, streamwise or tangential velocity, and nozzle exit diameter can influence these characteristics. Therefore, optimizing these parameters is essential to ensure acceptable spray quality for a given application [105]. The physical properties significantly influence spray characteristics, particularly affecting spray atomization. Key factors include viscosity, density, and surface tension [106].

Extensive studies have been conducted on swirl burners with cone-shaped sprays through experiments and simulations. Figure 14 illustrates the progression from a swirling jet to the complete atomization of the spray cone. At low mass flow rates, a jet with a circular cross-section forms near the nozzle. As the spray transitions, it adopts a flatter cross-section, resembling a twisted ribbon or thin sheet. This phenomenon typically occurs below the jet due to the interplay between the jet’s swirl strength and the fluid’s viscous stress [91]. Furthermore, Figure 15 provides a 3D snapshot of the flow field and droplets (black dots), with the red line indicating gas temperatures above 1500 K. It was presented by Jones et al. [46] when they investigated turbulent mixing, fuel spray dispersion, evaporation, and combustion within a gas turbine combustor geometry. Their comparison of the fuel spray model results accurately reflected the measured SMD and droplet velocity.

6.1. Spray Penetration

Spray penetration refers to the maximum distance a liquid or liquid-fuel spray reaches when injected from the nozzle head into stagnant air. This distance is determined by the balance between the kinetic energy of the initial fuel jet and the aerodynamic resistance of the surrounding gas. Typically, a compact and narrow spray achieves greater penetration, whereas a spray with lower penetration usually has a high cone angle and is well atomized, facing more air resistance. Fluid properties such as surface tension, viscosity, and density influence droplet size and the ease of atomization after being injected from an orifice. Figure 16 demonstrates how different biofuel blends affect particle penetration and compares the maximum length within the injector for these various blends [18].

6.2. Spray Angle and Dispersion

The cone angle of a spray is typically measured in experimental studies using image processing methods. In numerical simulations, it can be determined through either image processing or droplet density dispersion distribution. Khalid et al. [107] utilized direct photography to study the effects of diesel–water emulsification on mixture formation, combustion, and flame development in a burner system. Their findings indicated that the higher water content increased viscosity, resulting in greater penetration length and a smaller spray angle. Consequently, this led to a lower combustible mixture and reduced flame penetration. The spray angle and penetration were analyzed using image processing techniques.

Som et al. [4] performed a simulation study to examine the flow and spray behavior of biodiesel compared with petrodiesel. Figure 17 displays the droplet density results in a plotted graph, with a black box in the center representing the nozzle for reference. The dispersion distribution of the droplets from both fuels allows for a comparison of the spray angles. Fung [108] conducted both experimental and numerical studies on the spray characteristics of nasal spray devices, focusing on spray angle. The numerical analysis employed two different spray models, TAB and ETAB, and the results were compared with experimental data. The findings indicated a good agreement between the simulation and experimental results. During the later stages, specifically at injection times between 4 and 10 ms for a 6-bar case, the maximum observed difference was approximately 2 degrees.

6.3. Spray Droplets

Spray droplets formed in nozzles play a critical role in many industrial and engineering applications, including fuel injection in combustion engines and cooling systems. The atomization process involves the breakup of a liquid jet or sheet into fine droplets. This breakup occurs due to the interplay of surface tension, inertial forces, and aerodynamic interactions with the surrounding air. Primary atomization refers to the initial disintegration of the fluid into large droplets, while secondary atomization involves further fragmentation into smaller droplets as they travel through the environment. These processes are influenced by factors such as the nozzle geometry, fluid properties, and operating conditions, including flow pressure and velocity [58]. The size and distribution of the droplets are critical for determining the effectiveness of a spray system. Parameters such as the SMD are commonly used to quantify droplet sizes, relating the surface area to the volume of the droplets. Smaller droplets generally offer better mixing and evaporation, making them desirable in applications such as fuel injection, where efficient combustion depends on thorough mixing with air. However, producing smaller droplets often requires higher energy input, and this trade-off must be carefully managed [109].

Moreover, nozzle design significantly affects the spray characteristics, with different designs producing distinct spray patterns such as hollow cone, full cone, or flat spray. The flow regime within the nozzle, whether laminar or turbulent, also impacts droplet formation. Laminar flows tend to create larger, more uniform droplets, while turbulent flows enhance atomization, leading to smaller, more irregular droplets. Additionally, nozzle wear and erosion over time can degrade spray performance, highlighting the need for robust materials and maintenance [110]. To optimize spray systems, advanced diagnostic tools are employed to analyze droplet dynamics. Techniques such as laser Doppler anemometry (LDA) and phase Doppler particle analysis (PDPA) allow researchers to measure droplet size and velocity with high precision. These measurements are essential for improving system design and ensuring the desired performance in applications like agricultural spraying, where uniform coverage is critical, or combustion systems, where the droplet size influences fuel–air mixing and energy efficiency [111].

When liquid is mixed with air pressure for two-fluid nozzles or liquid pressure for hydraulic nozzles, potential energy is generated due to the nozzle’s geometry. This energy causes the liquid to spread into thin ligaments, which then break up into small spherical pieces called drops, droplets, or liquid particles. A droplet, a small spherical liquid particle, remains round due to surface tension. This same surface tension causes droplets and soap bubbles to attract each other into spherical shapes, resisting spreading. Sprays form when the interface between liquid and gas is disturbed, producing liquid droplets [18]. Figure 18 illustrates the particle size for various biofuel blends, while Figure 19 shows the spray pattern from a spray nozzle.

6.4. Sauter Mean Diameter (SMD)

The SMD is the diameter of a droplet that has the same surface-to-volume ratio as the entire spray. This measurement, often abbreviated as SMD or D₃₂ [58], is crucial for combustion applications. Predicting the SMD aids experimental studies by providing insights into specific modifications related to spray injections. Additionally, combustion performance and emission levels can be forecasted based on the SMD predictions. Smaller SMD values indicate a higher surface area per unit volume. This means that droplets will have more surface area, enhancing the efficiency of evaporation and mixture formation [112].

Jedelský and Jícha [113] conducted an experimental study on optimizing multi-hole effervescent atomizers for industrial burners utilizing oil-based fossil fuels and bio- or waste fuels to reduce emissions. Their phase Doppler anemometry results revealed that the D₃₂ profiles of these atomizers have an inversely bell-shaped distribution, with the minimum value located along the nozzle axis. Furthermore, Broukal and Hájek [16] conducted a validation study of an effervescent spray model with secondary atomization intended for use in a large-scale furnace. Their findings revealed an exaggerated bimodal distribution in drop sizes and inconsistencies in predicting the radial evolution of SMD. Despite this, there was a partial qualitative agreement in the radial evolution of the drop size distributions. Fung et al. [114] numerically investigated the spray breakup mechanism at low pressure in a nasal spray to validate experimental results. They simulated the secondary breakup using the Taylor Analogy Breakup (TAB) model and the spread parameters of the Rosin–Rammler distribution. The model predicted the spray plume shape and droplet size distribution (D₃₀, D₃₂) for low-pressure applications, and the validation data were utilized for further simulations. Yasin et al. [115] utilized CFD simulation to compare the Discrete Multicomponent (DM) model with the traditional Continuous Thermodynamics model (CTM) for spray formation using diesel and biodiesel. Their study found that biodiesel produced larger SMD droplet sizes than diesel, consistent with experimental measurements.

6.5. Influence of Fuel on Spray Characteristics

Lin et al. [106] performed an experimental study focusing on how different fuels influence spray characteristics. The fuels examined were EME20 (emulsified WME with 20% water), WME (waste cooking oil methyl esters), and diesel fuel. Figure 20 presents the measured spray cone angles for diesel fuel and WME. The graph indicates that there is no significant difference in spray angles, likely due to the higher viscosity of the emulsified fuel. Additionally, the EME spray angle was initially small during the early stages of atomization but increased significantly after 2 ms of spraying, potentially due to the micro-explosion of an emulsion droplet.

Figure 21 and Figure 22 display high-speed images of EMW20 and diesel fuel sprays at 380 °C. Initially, the EME spray angle was small (Figure 21-1 and Figure 22-1), but it gradually increased during atomization, around 2 ms (Figure 21-2 and Figure 22-2) to 3 ms (Figure 21-3 and Figure 22-3). The improvement in spraying is more notable as the temperature rises. From a microscopic view, in water-in-oil (w/o) biodiesel emulsions, water serves as the dispersed phase, while WME is the continuous phase. In the combustion chamber, the temperature increases during piston compression heat the droplets significantly, since water microcells are encapsulated in oil. This leads to a “micro-explosion” occurring when the saturated vapor pressure of the dispersed water phase reaches critical pressure, causing the droplets to burst. This micro-explosion results in secondary atomization, producing many fine droplets that evaporate quickly. As Figure 21 shows, the violent disintegration of the EME droplets in the later spraying stages generates fine secondary droplets with a larger physical volume, enhancing fuel spray characteristics. Consequently, the spray angle increases over time, as illustrated in Figure 20. Large EME droplets penetrating the combustion chamber undergo micro-explosions, enhancing local mixing and combustion efficiency [106].

Ing et al. [18] presented a numerical analysis of biofuel spray characteristics, focusing on blends of refined, RBDPO, and commercial diesel fuel (CDF) in a gas turbine. They compared various biofuel blends with pure diesel, categorizing the blends into B5, B10, B15, and B20 ratios. The study revealed that increasing the percentage of RBDPO results in higher SMD and particle penetration. Additionally, SMD was found to rise with increasing density, surface tension, and viscosity. Biodiesel demonstrated a higher SMD compared with standard diesel. Figure 23 illustrates the predicted SMD for different biodiesel blends in comparison with diesel.

6.6. Spray Analysis

Spray characteristics are typically assessed by analyzing SMD, spray angle, and spray penetration length. Various types of spray analysis methods are used to compare these parameters effectively. Battistoni et al. [5] conducted a simulation study comparing biodiesel and diesel. The findings indicated that biodiesel exhibited similar behavior with both conical and cylindrical geometrical shapes. In contrast, diesel displayed distinct breakup characteristics, being more responsive to different hole shapes. Diesel produced a faster and denser spray, maintaining compactness longer, at a conical hole but exhibited enhanced spray breakup at a cavitating hole. Figure 24 presents the penetration of diesel and biodiesel sprays for different hole shapes. The diesel spray from the conical hole demonstrates higher liquid penetration. Conversely, both diesel and biodiesel sprays from the cylindrical hole display lower and comparable penetration trends over time [5].

Yasin et al. [115] performed a numerical work comparing the DM model with the traditional CTM model for diesel and biodiesel fuels. They also made a semi-quantitative comparison between the predicted spray–combustion characteristics and optical measurements of a swirl-stabilized flame for both fuels. It was observed that the Sauter mean diameter D₃₂, normalized volume flux V, and normalized droplet density Dρ, along with all other spray variables, behaved as expected at their respective maximum values, except for D₃₂, which was normalized using the characteristic diameter, d, from the Rosin–Rammler distribution function in the spray boundary condition specification. The experimental findings showed that biodiesel had a droplet size, volume flux, and droplet density 20% higher than diesel. The simulation results similarly indicated higher droplet size, volume flux, and droplet density for biodiesel compared with diesel. Figure 25 depicts the normalized D₃₂, V, and Dρ.

However, Jones et al. [46] conducted a numerical study using LES to examine turbulent mixing, fuel spray dispersion, evaporation, and combustion within a gas turbine combustor geometry (the DLR Generic Single Sector Combustor). They analyzed spray statistics and how these are influenced by the combusting flow field, predicting droplet diameter, velocity, and SMD. The dispersed phase model (fuel spray) employed in the study accurately replicated SMD and droplet velocity using LES. They also highlighted the need for future research on the suitability of a Lagrangian formulation for the dispersed phase near the injector. These methods are generally applicable to dilute sprays with sufficiently small droplet sizes, considered point sources of mass, momentum, species, and energy [46]. Figure 26 shows the simulated SMD results at six different axial locations: z = 7 mm, 10 mm, 12.5 mm, 15 mm, 20 mm, and 30 mm. Figure 27 illustrates the radial profile of particle velocity along different axial locations, z = 7 mm, 10 mm, and 12.5 mm, based on the LES simulation.

7. Conclusions

Alternative fuels have gained widespread attention worldwide due to the need for a global environmental solution. Alternative fuels and orifice characteristics play a significant role in reducing greenhouse gas emissions. They collectively have the capability to enhance combustion performance and improve energy efficiency. The following conclusions can be summarized:

(i)   

Biodiesel from crude palm oil is capable of reducing the cavitation inside the orifice.

(ii)  

The Reynolds stress turbulence model and the Transition SST model are found to reasonably be the better turbulence models in the simulation of mixing.

(iii) 

Premix injectors can reduce emissions in the combustion process.

(iv) 

The application of swirling flow enhances the fuel and air mixing.

(v)  

Although cavitation could significantly improve the spray atomization, it may damage the orifice.

(vi) 

The cylindrical orifice is concluded to be a highly cavitating orifice, while the conical orifice is able to reduce the cavitation inside the orifice.

(vii)

Spray characteristics can enhance the overall combustion performance.

In summary, CPO biodiesel in spray combustion offers reduced emissions and renewable benefits but faces challenges such as higher viscosity, storage instability, material compatibility issues, and elevated NOx emissions. Further investigations into spray fineness and its impact on combustion dynamics are essential for advancing emission control and performance optimization.