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    Numerical and Optimization Studies on Reactivity Controlled Compression Ignition Engine With Hydrogen and Split Injections

    Source: Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture:;2025:;volume( 001 ):;issue: 002::page 21004-1
    Author:
    Amba Prasad Rao, G.
    ,
    Balaji, Lakkisetty Venkata
    DOI: 10.1115/1.4066761
    Publisher: The American Society of Mechanical Engineers (ASME)
    Abstract: Effective abatement of harmful tail-pipe emissions from fossil fuel run engines is achieved through low-temperature combustion strategies; the reactivity controlled compression ignition (RCCI) mode of operation has been successful among such concepts. The present work deals with numerical work performed using ansys forte software with n-heptane as a high-reactivity fuel and hydrogen in different proportions as a low-reactivity fuel, respectively. With total energy fixed, the amount of hydrogen is varied from 0% to 80% fuel injection is regulated accordingly. Pertinent engine in-cylinder parameters with patterns are extracted, emphasizing the combustion phenomena of RCCI operation with the lowest possible emissions targeted, with the combined effects of hydrogen induction, start of injection, and split injections. The contours of fuel vapor and emission parameters are obtained to relate the performance with emissions. It is noted that with a split injection strategy at 50/50 and 75/25 split strategy and 45–50% energy share from hydrogen, the NOx, soot reductions, and thermal efficiency penalty are in the range of about 5.5%, 24%, and 7.5%, respectively, also, with 30% exhaust gas recirculation (EGR), about 95% NOx reduction but with higher soot values. A 75/25 split and advanced injection timing of 25 deg bTDC resulted in the RCCI mode of operation with reduced soot emissions, and the use of EGR has resulted in high levels of soot and poor fuel efficiency. Among the models of machine learning tested, random forest regressor emerged as the most suitable, with higher R2 values, indicating better predictive capability.
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      Numerical and Optimization Studies on Reactivity Controlled Compression Ignition Engine With Hydrogen and Split Injections

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    • Journal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture

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    contributor authorAmba Prasad Rao, G.
    contributor authorBalaji, Lakkisetty Venkata
    date accessioned2025-04-21T10:09:53Z
    date available2025-04-21T10:09:53Z
    date copyright1/10/2025 12:00:00 AM
    date issued2025
    identifier issn2998-1638
    identifier otherjertb_1_2_021004.pdf
    identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4305626
    description abstractEffective abatement of harmful tail-pipe emissions from fossil fuel run engines is achieved through low-temperature combustion strategies; the reactivity controlled compression ignition (RCCI) mode of operation has been successful among such concepts. The present work deals with numerical work performed using ansys forte software with n-heptane as a high-reactivity fuel and hydrogen in different proportions as a low-reactivity fuel, respectively. With total energy fixed, the amount of hydrogen is varied from 0% to 80% fuel injection is regulated accordingly. Pertinent engine in-cylinder parameters with patterns are extracted, emphasizing the combustion phenomena of RCCI operation with the lowest possible emissions targeted, with the combined effects of hydrogen induction, start of injection, and split injections. The contours of fuel vapor and emission parameters are obtained to relate the performance with emissions. It is noted that with a split injection strategy at 50/50 and 75/25 split strategy and 45–50% energy share from hydrogen, the NOx, soot reductions, and thermal efficiency penalty are in the range of about 5.5%, 24%, and 7.5%, respectively, also, with 30% exhaust gas recirculation (EGR), about 95% NOx reduction but with higher soot values. A 75/25 split and advanced injection timing of 25 deg bTDC resulted in the RCCI mode of operation with reduced soot emissions, and the use of EGR has resulted in high levels of soot and poor fuel efficiency. Among the models of machine learning tested, random forest regressor emerged as the most suitable, with higher R2 values, indicating better predictive capability.
    publisherThe American Society of Mechanical Engineers (ASME)
    titleNumerical and Optimization Studies on Reactivity Controlled Compression Ignition Engine With Hydrogen and Split Injections
    typeJournal Paper
    journal volume1
    journal issue2
    journal titleJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture
    identifier doi10.1115/1.4066761
    journal fristpage21004-1
    journal lastpage21004-18
    page18
    treeJournal of Energy Resources Technology, Part B: Subsurface Energy and Carbon Capture:;2025:;volume( 001 ):;issue: 002
    contenttypeFulltext
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