Thermo-hydraulic performance prediction of a solar air heater with circular perforated absorber plate using Artificial Neural Network

Shetty, Shreyas P.; Nayak, Sadvidya S; Kumar, Shiva; Karanth, Vasudeva K

doi:10.1016/j.tsep.2021.100886

Cited by 21 publications

(4 citation statements)

References 47 publications

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“…The Levenberg-Marquardt (LM) algorithm is frequently used to solve nonlinear equations. Most of the time trial and error method is used to determine the number of hidden layers in an ANN model 32 , which is time-consuming. By using the thumb rule (Equation 1) 33 number of neurons for the hidden layer were selected, and the prediction was performed using MATLAB R2021b, where H n is the number of hidden neurons, M, N are the number of input and output variables and T n is the number of total training data.…”

Section: Ann Modelmentioning

confidence: 99%

Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites

et al. 2022

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Aluminium casting alloy LM4 (EN 1706 AC-45200) composites with TiB 2 (1, 2, and 3 wt.%) as reinforcements were produced using the two-stage stir casting method. OM and SEM study shows uniform and homogeneous reinforcement distribution in LM4 + TiB 2 composites. As-cast composites were subjected to single-stage solution treatment at 520°C for 2 h and multistage solution treatment at 495 and 520°C for 2 and 4 h, followed by hot water quenching at 60°C and aging at 100 and 200°C for different time intervals. The hardness of as-cast and artificially aged composites were compared in both conditions. Compared to as-cast LM4 alloy, 20-45% improvement in hardness was observed for LM4 + TiB 2 as-cast composites. 60-150% improvement in hardness was observed in artificially aged LM4 + 3 wt.% TiB 2 composites when aged at 100 and 200°C during peak aged conditions. TEM images confirmed the presence of primary strengthening solute-rich phases after age hardening treatment such as θ'-Al 2 Cu and θ"-Al 3 Cu, which are responsible for hardness increment. An artificial neural network (ANN) model was created to predict the hardness trend of these composite samples using MATLAB R2021b, and results proved that the ANN model developed can be utilized as an effective tool to predict the hardness of treated composite samples.

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Section: Ann Modelmentioning

confidence: 99%

Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites

et al. 2022

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“…Figure 9 shows the regression graphs of the model trained with normalized data with the LM algorithm and 30 hidden neurons. In the regression analysis of ANN, R-value shows the strength and direction of the relationship between predicted values and actual values [42]. If R-value is '1', it represents a perfect match between predicted and actual values; if R-value is '−1', it represents actual and predicted values are inversely proportional; if R-value is '0', it indicates no correlation exists [43].…”

Section: Artificial Neural Network (Ann)mentioning

confidence: 99%

Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks

Gowrishankar,

Doddapaneni,

Sharma

et al. 2023

Mater. Res. Express

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This research work highlights the prediction of hardness behaviour of age-hardened LM4 and its composites fabricated using a two-stage stir casting method with TiB2 and Si3N4. MATLAB - Artificial Neural Networks is used to predict the age-hardening behaviour of LM4 and its composites. Experiments (hardness and tensile tests) are conducted to collect data for training an ANN model as well as to investigate the effect of reinforcements and age-hardening treatment on LM4 and its composites. The results show that with an increment in the reinforcement wt.%, there is an enhancement in hardness and ultimate tensile strength (UTS) values within the monolithic composites. As-cast hybrid composites display a 37 to 54% improvement in hardness compared to as-cast LM4. Heat-treated samples, specifically those treated with peak aging with MSHT and 100°C aging, perform better than as-cast samples and other heat-treated samples in terms of UTS and hardness. Compared to as-cast LM4, MSHT, and 100°C aged samples display an 85 to 202% increment in VHN. Hybrid composites perform better in terms of hardness, while composites with 3 wt.% of TiB2 (L3TB) perform better in terms of UTS, peak aged (MSHT and 100°C aging) L3TB display 68% increment in UTS when compared to as-cast LM4. ANN model is developed and trained with five inputs (wt.% of TiB2, wt.% of Si3N4, type of solutionizing, aging temperature, and aging time) and one output (VHN) using different algorithms and a different number of hidden neurons to predict the age hardening behaviour of composites. Among them, Lavenberg-Marquardt (LM) training algorithm with normalized data and 30 hidden neurons performs well and shows a least average error of 1.588364. The confirmation test confirms that the trained ANN model can predict the output with an average %error of 0.14 using unseen data.

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“…The scholars obtained that the thermo-hydraulic performance of a solar air heater with circular perforated absorber plate could be predicted by utilization of ANN [26].…”

Section: Introductionmentioning

confidence: 99%

Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

et al. 2021

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Analyzing the combination of involving parameters impacting the efficiency of solar air heaters is an attractive research areas. In this study, cost-effective double-pass perforated glazed solar air heaters (SAHs) packed with wire mesh layers (DPGSAHM), and iron wools (DPGSAHI) were fabricated, tested and experimentally enhanced under different operating conditions. Forty-eight iron pieces of wool and fifteen steel wire mesh layers were located between the external plexiglass and internal glass, which is utilized as an absorber plate. The experimental outcomes show that the thermal efficiency enhances as the air mass flow rate increases for the range of 0.014–0.033 kg/s. The highest thermal efficiency gained by utilizing the hybrid optimized DPGSAHM and DPGSAHI was 94 and 97%, respectively. The exergy efficiency and temperature difference (∆T) indicated an inverse relationship with mass flow rate. When the DPGSAHM and DPGSAHI were optimized by the hybrid procedure and employing the Taguchi-artificial neural network, enhancements in the thermal efficiency by 1.25% and in exergy efficiency by 2.4% were delivered. The results show the average cost per kW (USD 0.028) of useful heat gained by the DPGSAHM and DPGSAHI to be relatively higher than some double-pass SAHs reported in the literature.

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Thermo-hydraulic performance prediction of a solar air heater with circular perforated absorber plate using Artificial Neural Network

Cited by 21 publications

References 47 publications

Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites

Artificial Neural Network for Predicting Hardness of Multistage Solutionized and Artificially Aged LM4 + TiB2 Composites

Prediction of age-hardening behaviour of LM4 and its composites using artificial neural networks

Efficiency Enhancement in Double-Pass Perforated Glazed Solar Air Heaters with Porous Beds: Taguchi-Artificial Neural Network Optimization and Cost–Benefit Analysis

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