Comparison of Flow Parameters in Internal Combustion Engines

Anetor, Lucky

doi:10.1007/s13369-012-0448-2

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…In a later stage, these step clouds were changed into leaf like structures and then into fully developed spray patterns. The induced vortex clouds were collapsed and changed into a fully developed spray pattern after 100 ms of the injection time [ 20 ].…”

Section: Resultsmentioning

confidence: 99%

Investigation of Vortex Clouds and Droplet Sizes in Heated Water Spray Patterns Generated by Axisymmetric Full Cone Nozzles

Naz

Sulaiman

Ariwahjoedi

et al. 2013

The Scientific World Journal

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The hot water sprays are an important part of many industrial processes, where the detailed knowledge of physical phenomena involved in jet transportation, interaction, secondary breakup, evaporation, and coalescence of droplets is important to reach more efficient processes. The objective of the work was to study the water spray jet breakup dynamics, vortex cloud formation, and droplet size distribution under varying temperature and load pressure. Using a high speed camera, the spray patterns generated by axisymmetric full cone nozzles were visualized as a function water temperature and load pressure. The image analysis confirmed that the spray cone angle and width do not vary significantly with increasing Reynolds and Weber numbers at early injection phases leading to increased macroscopic spray propagation. The formation and decay of semitorus like vortex clouds were also noticed in spray structures generated at near water boiling point temperature. For the nozzle with smallest orifice diameter (1.19 mm), these vortex clouds were very clear at 90°C heating temperature and 1 bar water load pressure. In addition, the sauter mean diameter (SMD) of the spray droplets was also measured by using Phase Doppler Anemometry (PDA) at different locations downstream of the nozzle exit. It was noticed that SMD varies slightly w.r.t. position when measured at room temperature whereas at higher temperature values, it became almost constant at distance of 55 mm downstream of the nozzle exit.

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Section: Resultsmentioning

confidence: 99%

Investigation of Vortex Clouds and Droplet Sizes in Heated Water Spray Patterns Generated by Axisymmetric Full Cone Nozzles

Naz

Sulaiman

Ariwahjoedi

et al. 2013

The Scientific World Journal

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“…Based on the engine load conditions, the parameters of an IC engine need to be optimized to increase the efficiency of the engine. Improvement in efficiency yields significant benefits in fuel consumption and lessens exhaust emissions [8]. Knowing engine load information during operation helps to adjust the parameters, since optimum parameters depend on the engine load.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Classification of Diesel Marine Engine Loads Using Machine Learning

Shahid

Kwon

2019

Sensors

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An engine control system is responsible for controlling the combustion parameters of an internal combustion engine to increase the efficiency of the engine. An optimized parameter setting of an engine control system is highly influenced by the engine load. Therefore, with a change in engine load, the parameter settings need to be updated for higher engine efficiency. Hence, to optimize parameter settings during operation, engine load information is necessary. In this paper, we propose a real-time engine load classification from sensed signals. For the classification, an artificial neural network is used and trained using processed, real, measured data. To that end, a magnetic pickup sensor extracts the rotational speed of the prime mover of a four-stroke V12 marine diesel engine. The measured signal is then converted into a crank angle degree (CAD) signal that shows the behavior of the combustion strokes of firing cylinders at a particular engine load. The CAD signals are considered an input feature to the designed network for classification of engine loads. For verification, we considered five classes of engine load, and the trained network classifies these classes with an accuracy of 99.4%.

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