Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks
Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is pro...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Ningbo [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015transfer abstract |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: Role of sulfur in combating arsenic stress through upregulation of important proteins, and - Amna, Syeda ELSEVIER, 2020, an international journal on the science and technology of wet and dry particulate systems, Amsterdam [u.a.] |
|---|---|
| Übergeordnetes Werk: |
volume:281 ; year:2015 ; pages:173-183 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.powtec.2015.04.058 |
|---|
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ELV029395585 |
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| 520 | |a Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. | ||
| 520 | |a Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. | ||
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10.1016/j.powtec.2015.04.058 doi GBVA2015023000024.pica (DE-627)ELV029395585 (ELSEVIER)S0032-5910(15)00343-5 DE-627 ger DE-627 rakwb eng 660 660 DE-600 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Zhao, Ningbo verfasserin aut Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks 2015transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Wen, Xueyou oth Yang, Jialong oth Li, Shuying oth Wang, Zhitao oth Enthalten in Elsevier Science Amna, Syeda ELSEVIER Role of sulfur in combating arsenic stress through upregulation of important proteins, and 2020 an international journal on the science and technology of wet and dry particulate systems Amsterdam [u.a.] (DE-627)ELV005093252 volume:281 year:2015 pages:173-183 extent:11 https://doi.org/10.1016/j.powtec.2015.04.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 281 2015 173-183 11 045F 660 |
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10.1016/j.powtec.2015.04.058 doi GBVA2015023000024.pica (DE-627)ELV029395585 (ELSEVIER)S0032-5910(15)00343-5 DE-627 ger DE-627 rakwb eng 660 660 DE-600 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Zhao, Ningbo verfasserin aut Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks 2015transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Wen, Xueyou oth Yang, Jialong oth Li, Shuying oth Wang, Zhitao oth Enthalten in Elsevier Science Amna, Syeda ELSEVIER Role of sulfur in combating arsenic stress through upregulation of important proteins, and 2020 an international journal on the science and technology of wet and dry particulate systems Amsterdam [u.a.] (DE-627)ELV005093252 volume:281 year:2015 pages:173-183 extent:11 https://doi.org/10.1016/j.powtec.2015.04.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 281 2015 173-183 11 045F 660 |
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10.1016/j.powtec.2015.04.058 doi GBVA2015023000024.pica (DE-627)ELV029395585 (ELSEVIER)S0032-5910(15)00343-5 DE-627 ger DE-627 rakwb eng 660 660 DE-600 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Zhao, Ningbo verfasserin aut Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks 2015transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Wen, Xueyou oth Yang, Jialong oth Li, Shuying oth Wang, Zhitao oth Enthalten in Elsevier Science Amna, Syeda ELSEVIER Role of sulfur in combating arsenic stress through upregulation of important proteins, and 2020 an international journal on the science and technology of wet and dry particulate systems Amsterdam [u.a.] (DE-627)ELV005093252 volume:281 year:2015 pages:173-183 extent:11 https://doi.org/10.1016/j.powtec.2015.04.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 281 2015 173-183 11 045F 660 |
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10.1016/j.powtec.2015.04.058 doi GBVA2015023000024.pica (DE-627)ELV029395585 (ELSEVIER)S0032-5910(15)00343-5 DE-627 ger DE-627 rakwb eng 660 660 DE-600 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Zhao, Ningbo verfasserin aut Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks 2015transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Wen, Xueyou oth Yang, Jialong oth Li, Shuying oth Wang, Zhitao oth Enthalten in Elsevier Science Amna, Syeda ELSEVIER Role of sulfur in combating arsenic stress through upregulation of important proteins, and 2020 an international journal on the science and technology of wet and dry particulate systems Amsterdam [u.a.] (DE-627)ELV005093252 volume:281 year:2015 pages:173-183 extent:11 https://doi.org/10.1016/j.powtec.2015.04.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 281 2015 173-183 11 045F 660 |
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10.1016/j.powtec.2015.04.058 doi GBVA2015023000024.pica (DE-627)ELV029395585 (ELSEVIER)S0032-5910(15)00343-5 DE-627 ger DE-627 rakwb eng 660 660 DE-600 630 640 580 VZ BIODIV DE-30 fid 42.00 bkl Zhao, Ningbo verfasserin aut Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks 2015transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. Wen, Xueyou oth Yang, Jialong oth Li, Shuying oth Wang, Zhitao oth Enthalten in Elsevier Science Amna, Syeda ELSEVIER Role of sulfur in combating arsenic stress through upregulation of important proteins, and 2020 an international journal on the science and technology of wet and dry particulate systems Amsterdam [u.a.] (DE-627)ELV005093252 volume:281 year:2015 pages:173-183 extent:11 https://doi.org/10.1016/j.powtec.2015.04.058 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV 42.00 Biologie: Allgemeines VZ AR 281 2015 173-183 11 045F 660 |
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| author |
Zhao, Ningbo |
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Role of sulfur in combating arsenic stress through upregulation of important proteins, and |
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Role of sulfur in combating arsenic stress through upregulation of important proteins, and |
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Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks |
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Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks |
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Zhao, Ningbo |
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Role of sulfur in combating arsenic stress through upregulation of important proteins, and |
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Role of sulfur in combating arsenic stress through upregulation of important proteins, and |
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modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks |
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Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks |
| abstract |
Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. |
| abstractGer |
Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. |
| abstract_unstemmed |
Due to the fact that the viscosity of nanofluids can be affected by many factors, it is difficult to establish an accurate prediction model using traditional model-driven methods. To address this problem, a new viscosity prediction approach based on radial basis function (RBF) neural networks is proposed in this paper. Two RBF neural networks are proposed, one with 5 input variables, the other with 4 input variables. Both models take into account the effects of nanoparticle volume concentration, nanoparticle diameter, nanoparticle density and the viscosity of base fluid, while the 5-input model also considers the effect of temperature. Two different types of nanofluids, namely Al2O3–water and CuO–water, are used to evaluate the effectiveness of the proposed models. The comparisons demonstrate that the predicted viscosity of RBF neural networks agree well with the experimental data, which outperforms many existing theoretical and empirical models. The results also show that the prediction performance of RBF neural networks can be further improved when the temperature is added as an input variable. |
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Modeling and prediction of viscosity of water-based nanofluids by radial basis function neural networks |
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https://doi.org/10.1016/j.powtec.2015.04.058 |
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Wen, Xueyou Yang, Jialong Li, Shuying Wang, Zhitao |
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