A Bayesian computational technique for learning the airflow resistance of acoustical fibrous materials at high temperatures.
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| Title: | A Bayesian computational technique for learning the airflow resistance of acoustical fibrous materials at high temperatures. |
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| Authors: | Samarasinghe, Thamasha1 (AUTHOR) 1933914@alumni.brunel.ac.uk, Herath, Sumudu2 (AUTHOR) |
| Source: | Building Acoustics. Mar2025, Vol. 32 Issue 1, p91-108. 18p. |
| Subjects: | Kriging, Acoustical engineering, Acoustical materials, Heat resistant materials, Materials science |
| Abstract: | This study investigates the acoustic performance of various porous materials at elevated temperatures, employing Constrained Gaussian Process Regression (CGPR) to model the relationship between specific airflow resistance and the absolute temperature. Experimental data on six fibrous material samples at 600°C are used to develop and compare CGPR models against conventional Power Law Regression (PLR) methods. The developed CGPR incorporates constraints such as boundedness, monotonicity and convexity/concavity derived from the evident relationships and prior knowledge of the specific airflow resistance versus temperature variations. The results of this study prove the outperformance of the developed CGPR over PLR methods in terms of data efficiency, predictive accuracy, uncertainty quantification, overfitting recovery and extrapolation capability. This comparative analysis outlines a significant improvement in the predictive accuracy of CGPR, achieving improved coefficient of determination values compared to PLR. CGPR also furnishes a direct strategy to quantify the uncertainty of predictions which is vital for applications at elevated temperatures. Additionally, CGPR offers valuable insights into sound absorption behaviour, highlighting its applicability in thermal acoustics and materials engineering. Prospective research avenues stem from this research as the developed CGPR technique has the potential to replace various modelling techniques in materials science and acoustic engineering applications. [ABSTRACT FROM AUTHOR] |
| Copyright of Building Acoustics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 183273111 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Bayesian computational technique for learning the airflow resistance of acoustical fibrous materials at high temperatures. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Samarasinghe%2C+Thamasha%22">Samarasinghe, Thamasha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> 1933914@alumni.brunel.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Herath%2C+Sumudu%22">Herath, Sumudu</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Building+Acoustics%22">Building Acoustics</searchLink>. Mar2025, Vol. 32 Issue 1, p91-108. 18p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Kriging%22">Kriging</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustical+engineering%22">Acoustical engineering</searchLink><br /><searchLink fieldCode="DE" term="%22Acoustical+materials%22">Acoustical materials</searchLink><br /><searchLink fieldCode="DE" term="%22Heat+resistant+materials%22">Heat resistant materials</searchLink><br /><searchLink fieldCode="DE" term="%22Materials+science%22">Materials science</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This study investigates the acoustic performance of various porous materials at elevated temperatures, employing Constrained Gaussian Process Regression (CGPR) to model the relationship between specific airflow resistance and the absolute temperature. Experimental data on six fibrous material samples at 600°C are used to develop and compare CGPR models against conventional Power Law Regression (PLR) methods. The developed CGPR incorporates constraints such as boundedness, monotonicity and convexity/concavity derived from the evident relationships and prior knowledge of the specific airflow resistance versus temperature variations. The results of this study prove the outperformance of the developed CGPR over PLR methods in terms of data efficiency, predictive accuracy, uncertainty quantification, overfitting recovery and extrapolation capability. This comparative analysis outlines a significant improvement in the predictive accuracy of CGPR, achieving improved coefficient of determination values compared to PLR. CGPR also furnishes a direct strategy to quantify the uncertainty of predictions which is vital for applications at elevated temperatures. Additionally, CGPR offers valuable insights into sound absorption behaviour, highlighting its applicability in thermal acoustics and materials engineering. Prospective research avenues stem from this research as the developed CGPR technique has the potential to replace various modelling techniques in materials science and acoustic engineering applications. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Building Acoustics is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=183273111 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/1351010X241305948 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 18 StartPage: 91 Subjects: – SubjectFull: Kriging Type: general – SubjectFull: Acoustical engineering Type: general – SubjectFull: Acoustical materials Type: general – SubjectFull: Heat resistant materials Type: general – SubjectFull: Materials science Type: general Titles: – TitleFull: A Bayesian computational technique for learning the airflow resistance of acoustical fibrous materials at high temperatures. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Samarasinghe, Thamasha – PersonEntity: Name: NameFull: Herath, Sumudu IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 1351010X Numbering: – Type: volume Value: 32 – Type: issue Value: 1 Titles: – TitleFull: Building Acoustics Type: main |
| ResultId | 1 |
