Artificial neural networks model for predicting the behavior of different injection pressure characteristics powered by blend of biofuel‐nano emulsion

Venkatesan, Elumalai Perumal; Moorthy, R. Krishna; Parthasarathy, M.; Samuel, Olusegun David; Owamah, H.I.; Saleel, C. Ahamed; Enweremadu, Christopher C.; Reddy, M. Sreenivasa; Afzal, Asif

doi:10.1002/ese3.1144

Cited by 34 publications

(17 citation statements)

References 71 publications

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“…In Table 6, mesh-independence analysis was used to optimize the mesh size. The mesh size was adjusted when further meshing did not affect the simulated data [52][53][54][55][56]. Two million cells were chosen because there was no additional enhancement in the solution after such count, based on consistent with the experimental information.…”

Section: Methodology Of Cfd Analysismentioning

confidence: 99%

An Analysis of the Effects of Nanofluid-Based Serpentine Tube Cooling Enhancement in Solar Photovoltaic Cells for Green Cities

Jose¹,

Shrivastava

Soni³

et al. 2023

Journal of Nanomaterials

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In this work, the impact of the serpentine copper tube heat exchanger with nanofluids on 100 W solar photovoltaic thermal collectors (PV/T) was analyzed experimentally and numerically. The cooling fluids assessed in this system were distilled water, Al2O3 0.1%, and Al2O3 0.2% based nanofluids. Tests were accomplished at diverse coolant mass flow rate in India’s summer days of 2018. A computational fluid dynamics (CFD) investigation was carried out to perform a parametric study, identify surface and exit T profiles, and examine the cooling effectiveness. The impact of mass flow rate of nanofluid on the outside T and Reynolds number were studied. The Reynolds number obtained in the flow experiments and CFD analysis was in the range of 900–1,300. The maximum irreversibility occurred while using water, whereas minimum irreversibility obtained Al2O3 0.2% nanofluid. Exergy efficiency was found to be increased from 20% to 36% during the day. It was identified that the increase in PV/T scheme led to higher exergy losses. The thermal efficiency of a water-based cooling system resulted in 53.61%. Meanwhile, Al2O3 0.1% and Al2O3 0.2% based coolants provided 69.45% and 71.02%, respectively. A good agreement was obtained between the experimental results and the computer model.

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Section: Methodology Of Cfd Analysismentioning

confidence: 99%

An Analysis of the Effects of Nanofluid-Based Serpentine Tube Cooling Enhancement in Solar Photovoltaic Cells for Green Cities

Jose¹,

Shrivastava

Soni³

et al. 2023

Journal of Nanomaterials

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“…Since GP and SVM-Schotastic model performed the best among the other models for FS and CS for this dataset, sensitivity analysis was carried out on it by changing the input combination and taking out one input parameter at a time, as shown in Table 9 and Table 10 . Statistical assessment metrics such as CC, MAE, and RMSE were used to assess each model’s performance [ 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 ]. Table 9 and Table 10 , demonstrates that the number of curing days followed by CA, C, w and MP is critical in predicting the flexural and compressive strength of a concrete mix.…”

Section: Sensitivity Analysismentioning

confidence: 99%

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

et al. 2022

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The purpose of the research is to predict the compressive and flexural strengths of the concrete mix by using waste marble powder as a partial replacement of cement and sand, based on the experimental data that was acquired from the laboratory tests. In order to accomplish the goal, the models of Support vector machines, Support vector machines with bagging and Stochastic, Linear regression, and Gaussian processes were applied to the experimental data for predicting the compressive and flexural strength of concrete. The effectiveness of models was also evaluated by using statistical criteria. Therefore, it can be inferred that the gaussian process and support vector machine methods can be used to predict the respective outputs, i.e., flexural and compressive strength. The Gaussian process and Support vector machines Stochastic predicts better outcomes for flexural and compressive strength because it has a higher coefficient of correlation (0.8235 and 0.9462), lower mean absolute and root mean squared error values as (2.2808 and 1.8104) and (2.8527 and 2.3430), respectively. Results suggest that all applied techniques are reliable for predicting the compressive and flexural strength of concrete and are able to reduce the experimental work time. In comparison to input factors for this data set, the number of curing days followed by the CA, C, FA, w, and MP is essential in predicting the flexural and compressive strength of a concrete mix for this data set.

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“…The static model is fitted on the surface and based on those observations; independent variables are analyzed. The contour plots exhibit the optimum values of responses [ 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ].…”

Section: Experimental Designmentioning

confidence: 99%

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

Jagadeesan¹,

Selvaraj

Santhosh

et al. 2022

Materials

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The tribological performance of a glass fiber reinforced polyamide66 (GFRPA66) composite with varying fiber weight percentage (wt.%) [30 wt.% and 35 wt.%] is investigated in this study using a pin-on-disc tribometer. GFRPA66 composite specimens in the form of pins with varying percentages of fiber viz., 30 wt.% and 35 wt.% are fabricated by an injection molding process. Tribological performances, such as coefficient of friction (COF) and the specific wear rate (SWR), are investigated. The factors affecting the wear of GFRPA66 composites [with 30 wt.% and 35 wt.% reinforcements] are identified based on the process parameters such as load, sliding velocity, and sliding distance. Design Expert 13.0 software is used for the experimental data analysis, based on the design of experiments planned in accordance with the central composite design (CCD) of the response surface methodology (RSM) technique. The significance of the obtained results are analyzed using analysis of variance (ANOVA) techniques. To attain minimum SWR and COF, the wear performance is optimized in dry sliding conditions. The analysis of experimental data revealed that SWR and COF increased with increasing load, sliding velocity, and sliding distance for GFRPA66 [30 wt.%], but decreased with increasing polyamide weight percentage. The SWR for a maximum load of 80 N, and for a sliding velocity of 0.22 m/s, and a sliding distance of 3500 m for GFRPA66 composite specimens with 30 wt.% reinforcements are found to be 0.0121 m3/Nm, while the SWR for the same set of parameters for GFRPA66 composite specimens with 35 wt.% reinforcements are found to be 0.0102 m3/Nm. The COF for the GFRPA66 composite specimens with 30 wt.% reinforcements for the above set of parameters is found to be 0.37, while the GFRPA66 composite specimens with 35 wt.% reinforcements showed significant improvement in wear performance with a reduction in COF to 0.25. Finally, using a scanning electron microscope (SEM), the worn surfaces of the GFRPA66 are examined and interpreted.

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Artificial neural networks model for predicting the behavior of different injection pressure characteristics powered by blend of biofuel‐nano emulsion

Cited by 34 publications

References 71 publications

An Analysis of the Effects of Nanofluid-Based Serpentine Tube Cooling Enhancement in Solar Photovoltaic Cells for Green Cities

An Analysis of the Effects of Nanofluid-Based Serpentine Tube Cooling Enhancement in Solar Photovoltaic Cells for Green Cities

Machine Learning Techniques for Evaluating Concrete Strength with Waste Marble Powder

Response Surface Methodology Based Optimization of Test Parameter in Glass Fiber Reinforced Polyamide 66 for Dry Sliding, Tribological Performance

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