Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry
This study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10400 |
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| author | Bianca-Mihaela Cășeriu Manuela-Rozalia Gabor Petruța Blaga Cristina Veres |
| author_facet | Bianca-Mihaela Cășeriu Manuela-Rozalia Gabor Petruța Blaga Cristina Veres |
| author_sort | Bianca-Mihaela Cășeriu |
| collection | DOAJ |
| description | This study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on materials from six different classes and employs a multidisciplinary approach, including Mann–Whitney U tests, Kruskal–Wallis analysis with Dunn’s post hoc multiple comparisons and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase. |
| format | Article |
| id | doaj-art-2af98f5338e346caabdac770338ccc90 |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2af98f5338e346caabdac770338ccc902025-08-20T02:08:02ZengMDPI AGApplied Sciences2076-34172024-11-0114221040010.3390/app142210400Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive IndustryBianca-Mihaela Cășeriu0Manuela-Rozalia Gabor1Petruța Blaga2Cristina Veres3Doctoral School of I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu Street, 38, 540142 Targu Mures, RomaniaDoctoral School of I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu Street, 38, 540142 Targu Mures, RomaniaDoctoral School of I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu Street, 38, 540142 Targu Mures, RomaniaDoctoral School of I.O.S.U.D., George Emil Palade University of Medicine, Pharmacy, Science and Technology of Targu Mures, Gheorghe Marinescu Street, 38, 540142 Targu Mures, RomaniaThis study proposes a qualitative analysis for identifying the best predictors for ensuring passive noise control, aiming to achieve superior acoustic comfort in transportation systems. The study is based on real experimental data, collected through acoustic measurements performed by the authors on materials from six different classes and employs a multidisciplinary approach, including Mann–Whitney U tests, Kruskal–Wallis analysis with Dunn’s post hoc multiple comparisons and multilinear regression. This research presents an analysis and evaluation of how the physical properties of various materials influence acoustic comfort, acoustic absorption class and absorption class performance and proposes quantitative models for material selection to address sustainable strategies in the automotive industry. The results highlight significant differences between material categories in terms of acoustic absorption properties and demonstrate the importance of rigorous material selection in vehicle design to enhance acoustic comfort. Additionally, the research contributes to the development of predictive models that estimate acoustic performance based on the physical properties of materials, providing a basis for optimizing material selection in the design phase.https://www.mdpi.com/2076-3417/14/22/10400sound-absorption propertiesbest predictors of acoustic materialsacoustic performancemedium frequencystatistical methodsexperimental data |
| spellingShingle | Bianca-Mihaela Cășeriu Manuela-Rozalia Gabor Petruța Blaga Cristina Veres Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry Applied Sciences sound-absorption properties best predictors of acoustic materials acoustic performance medium frequency statistical methods experimental data |
| title | Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry |
| title_full | Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry |
| title_fullStr | Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry |
| title_full_unstemmed | Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry |
| title_short | Quantitative Analysis of Predictors of Acoustic Materials for Noise Reduction as Sustainable Strategies for Materials in the Automotive Industry |
| title_sort | quantitative analysis of predictors of acoustic materials for noise reduction as sustainable strategies for materials in the automotive industry |
| topic | sound-absorption properties best predictors of acoustic materials acoustic performance medium frequency statistical methods experimental data |
| url | https://www.mdpi.com/2076-3417/14/22/10400 |
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