Experimental investigation on a 3000 bar fuel injection system for a SCR-free non-road diesel engine

Boccardo, Giulio; Millo, Federico; Piano, Andrea; Arnone, Luigi; Manelli, Stefano; Fagg, Simon; Gatti, P.; Herrmann, Olaf Erik; Queck, Dirk; Weber, J.

doi:10.1016/j.fuel.2019.01.122

Cited by 33 publications

(15 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Limitations to greenhouse gases (CO 2 ), gaseous pollutants, and noise emissions will be increasingly severe, forcing the automotive industry to invest in more innovative technologies for their reduction [10,[27][28][29][30]. Real driving emissions tests are being adopted in the major global economic zones as this strategy expands the ICE operational range in which the pollutant emissions must be kept below the approval limits [31][32][33].…”

Section: What Can the New Generation Of Ice Provide?mentioning

confidence: 99%

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

2019

View full text Add to dashboard Cite

show abstract

Section: What Can the New Generation Of Ice Provide?mentioning

confidence: 99%

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

2019

View full text Add to dashboard Cite

show abstract

“…Currently, with the more advanced technology, it is possible to adopt an ultrahigh injection pressure up to 3000 bar and even to 5000 bar [1]. The investigations show that high injection pressure can effectively improve the soot-NOx trade-off relationship, which helps to relieve the dependence of engine after-treatment systems [2]. DICI combustion involves a series of complicated processes, including the internal nozzle flow, spray breakup and atomization, air-fuel mixing, and turbulent combustion processes.…”

Section: Introductionmentioning

confidence: 99%

Investigation of the Engine Combustion Network Spray A Characteristics using Eulerian and Lagrangian Models

Liu¹,

Al-lehaibi²,

Im³

2022

SAE Technical Paper Series

View full text Add to dashboard Cite

This work presents a numerical study of the Spray A (n-dodecane) characteristics using Eulerian and Lagrangian models in a finitevolume framework. The standard k- turbulence model was applied for the spray simulations. For Eulerian simulations, the X-ray measured injector geometries from the Engine Combustion Network (ECN) were employed. The High-Resolution Interface Capturing (HRIC) scheme coupled with a cavitation model was utilized to track the fluid-gas interface. Simulations under both the cool and hot ambient conditions were performed. The effects of various grid sizes, turbulence constants, nozzle geometries, and initial gas volume within the injector sac on the modeling results were evaluated. As indicated by the Eulerian simulation results, no cavitation was observed for the Spray A injector; a minimum mesh size of 15.6 m could achieve a reasonably convergent criterion; the nominal nozzle geometry predicted similar results to the X-ray measured nozzle geometry. For both the Eulerian and Lagrangian simulations, the higher C1 value of the turbulence model resulted in the lower turbulent kinetic energy, longer jet penetration, and spray cone angle. Since the Eulerian-Lagrangian coupled method has the advantage over spray distribution at the nozzle exit, it predicted a significantly better near-nozzle mixture distribution compared to the conventional Lagrangian model at a non-vaporizing condition. By employing an initial gas volume fraction of 30% within the injector sac as recommended by the Engine Combustion Network committee, the Eulerian-Lagrangian coupled method could well reproduce the experimental rate of injection profile, fuel mixture distributions, and spray penetrations at a vaporizing condition. Furthermore, the higher injection pressure promoted the vapor penetration, but it had limited effects on the liquid penetration owing to the competitive relationship between the higher spray momentum and evaporation rate. The higher ambient temperature reduced the liquid penetration for the higher evaporation rate, but it had limited effects on the vapor penetration since the spray momentum and ambient density were kept unchanged.

show abstract

“…Additional complexities in Diesel fuel injection exist, due to strong property variations the fuel is subjected to. Indicatively, for modern Diesel engines, a pressure variation of more than 2000 bar (modern systems may even reach 3000 bar 17 ) to effectively 0bar (cavitating regions in the fuel injector) and a temperature variation from 363 K (high pressure side of the fuel pump) to more than 1000 K (engine cylinder) are expected. Commonly used assumptions of, for example, incompressible fluids are no longer valid and non-ideal thermodynamic effects, such as viscous heating and Joule-Thomson cooling become important.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

Koukouvinis

Rodríguez

Hwang

et al. 2021

International Journal of Engine Research

View full text Add to dashboard Cite

The present work investigates the application of Machine Learning and Artificial Neural Networks for tackling the complex issue of transcritical sprays, which are relevant to modern compression-ignition engines. Such conditions imply the departure of the classical thermodynamic perspective of ideal gas or incompressible liquid, necessitating the use of costly and elaborate thermodynamic closures to describe property variation and simulation methods. Machine Learning can assist in several ways in speeding up such calculations, either as a compact, trained thermodynamic model that can be coupled to the flow solver, or as a surrogate predictive tool of spray characteristics. In this work, such applications are demonstrated and their performance is assessed against more traditional approaches. Such applications involve the prediction of macroscopic spray characteristics, for example, the spray penetration over time, or the spray distribution in space and time, and predictions of fluid properties for the thermodynamic states encountered in such applications. Macroscopic characteristics can be adequately predicted by relatively simple network structures, involving just a hidden layer of 3–4 neurons, whereas prediction of thermodynamic states requires several layers of 5–20 neurons each. The results of integrating Artificial Neural Networks in transcritical sprays are rather promising; prediction of thermodynamic properties at pressures greater than 1bar has effectively zero error, yielding simulations indistinguishable from standard tabulated approaches with minimal overhead. When used as a regression method for time-histories either of spray characteristics or spray distributions, the results are within experimental uncertainty of similar experiments, not included in the training dataset.

show abstract

Experimental investigation on a 3000 bar fuel injection system for a SCR-free non-road diesel engine

Cited by 33 publications

References 11 publications

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

Investigation of the Engine Combustion Network Spray A Characteristics using Eulerian and Lagrangian Models

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

Contact Info

Product

Resources

About

Experimental investigation on a 3000 bar fuel injection system for a SCR-free non-road diesel engine

Cited by 33 publications

References 11 publications

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

Why the Development of Internal Combustion Engines Is Still Necessary to Fight against Global Climate Change from the Perspective of Transportation

Investigation of the Engine Combustion Network Spray A Characteristics using Eulerian and Lagrangian Models

Machine Learning and transcritical sprays: A demonstration study of their potential in ECN Spray-A

Contact Info

Product

Resources

About

Experimental investigation on a 3000 bar fuel injection system for a SCR-free non-road diesel engine