Optimization of Cu 2 O Nano-Additive-Doped Diesel Engine Performance via Physics-Informed Hybrid GPR Framework.
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| Title: | Optimization of Cu 2 O Nano-Additive-Doped Diesel Engine Performance via Physics-Informed Hybrid GPR Framework. |
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| Authors: | Orman, Recep Cagri1 (AUTHOR) |
| Source: | Energies (19961073). Apr2026, Vol. 19 Issue 7, p1603. 15p. |
| Subject Terms: | *Nanoparticles, *Automobile engine performance, *Gaussian processes, *Multi-objective optimization, *Machine learning, *Carbon emissions |
| Abstract: | In this study, a novel "Physics-Informed Hybrid Machine Learning" framework was developed to model and optimize the complex combustion and carbon-based emission characteristics of Cu2O nano-additive doped diesel fuel. To reduce reliance on purely empirical correlations, the proposed framework integrates alterations in fuel physical properties into the prediction loop, thereby enhancing physical consistency and model generalizability. The methodology comprises data pre-processing, modeling via Gaussian Process Regression (GPR) with an Automatic Relevance Determination (ARD) kernel, and multi-objective optimization using NSGA-II. Experimental tests were conducted at a constant engine speed of 2000 rpm under varying load conditions. The developed hybrid model exhibited high predictive accuracy, particularly for performance metrics and gaseous emissions (e.g., R2 > 0.95 for BSFC and CO). ARD-based feature importance analysis confirmed that nano-additive dosage plays a critical role in the fine-tuning of emissions. Crucially, the optimization algorithm identified a nano-additive dosage of ~29 ppm and an engine load of 15.5 Nm as the optimal operating point for the simultaneous improvement of performance and carbonaceous emissions. This finding, exploring the unmeasured design space, demonstrates the framework's capability to discover optimal conditions beyond discrete experimental points. [ABSTRACT FROM AUTHOR] |
| Database: | Energy & Power Source |
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| FullText | Links: – Type: pdflink Text: Availability: 1 |
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| Header | DbId: enr DbLabel: Energy & Power Source An: 192959007 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Optimization of Cu 2 O Nano-Additive-Doped Diesel Engine Performance via Physics-Informed Hybrid GPR Framework. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Orman%2C+Recep+Cagri%22">Orman, Recep Cagri</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Energies+%2819961073%29%22">Energies (19961073)</searchLink>. Apr2026, Vol. 19 Issue 7, p1603. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Nanoparticles%22">Nanoparticles</searchLink><br />*<searchLink fieldCode="DE" term="%22Automobile+engine+performance%22">Automobile engine performance</searchLink><br />*<searchLink fieldCode="DE" term="%22Gaussian+processes%22">Gaussian processes</searchLink><br />*<searchLink fieldCode="DE" term="%22Multi-objective+optimization%22">Multi-objective optimization</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+emissions%22">Carbon emissions</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In this study, a novel "Physics-Informed Hybrid Machine Learning" framework was developed to model and optimize the complex combustion and carbon-based emission characteristics of Cu2O nano-additive doped diesel fuel. To reduce reliance on purely empirical correlations, the proposed framework integrates alterations in fuel physical properties into the prediction loop, thereby enhancing physical consistency and model generalizability. The methodology comprises data pre-processing, modeling via Gaussian Process Regression (GPR) with an Automatic Relevance Determination (ARD) kernel, and multi-objective optimization using NSGA-II. Experimental tests were conducted at a constant engine speed of 2000 rpm under varying load conditions. The developed hybrid model exhibited high predictive accuracy, particularly for performance metrics and gaseous emissions (e.g., R2 > 0.95 for BSFC and CO). ARD-based feature importance analysis confirmed that nano-additive dosage plays a critical role in the fine-tuning of emissions. Crucially, the optimization algorithm identified a nano-additive dosage of ~29 ppm and an engine load of 15.5 Nm as the optimal operating point for the simultaneous improvement of performance and carbonaceous emissions. This finding, exploring the unmeasured design space, demonstrates the framework's capability to discover optimal conditions beyond discrete experimental points. [ABSTRACT FROM AUTHOR] |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=enr&AN=192959007 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3390/en19071603 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 1603 Subjects: – SubjectFull: Nanoparticles Type: general – SubjectFull: Automobile engine performance Type: general – SubjectFull: Gaussian processes Type: general – SubjectFull: Multi-objective optimization Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Carbon emissions Type: general Titles: – TitleFull: Optimization of Cu 2 O Nano-Additive-Doped Diesel Engine Performance via Physics-Informed Hybrid GPR Framework. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Orman, Recep Cagri IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: Apr2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 19961073 Numbering: – Type: volume Value: 19 – Type: issue Value: 7 Titles: – TitleFull: Energies (19961073) Type: main |
| ResultId | 1 |
