Research

16 pages, 3307 KiB

Open AccessArticle

Investigation of Condensate Retention on Horizontal Pin-Fin Tubes Using Water-Propanol Mixture

by Hafiz Muhammad Habib, Hafiz Muhammad Ali and Muhammad Usman

Sustainability 2022, 14(2), 835; https://0-doi-org.brum.beds.ac.uk/10.3390/su14020835 - 12 Jan 2022

Cited by 5 | Viewed by 1318

Condensers are an integral part of air conditioning systems. The thermal efficiency of condensers solely depends on the rate of heat transfer from the cooling medium. Fin tubes are extensively used for heat transfer applications due to their enhanced heat transfer capabilities. Fins [...] Read more.

Condensers are an integral part of air conditioning systems. The thermal efficiency of condensers solely depends on the rate of heat transfer from the cooling medium. Fin tubes are extensively used for heat transfer applications due to their enhanced heat transfer capabilities. Fins provide appreciable drainage because surface tension produces pressure gradients. Much research, contributed by several scientists, has focused on adjusting parameters, such as fin design, flow rates and retention angles. In this study, a setup with an observing hole was used to inspect the influence on retention angle of adjusting the flow rates of the fluid. The increase in retention angle was examined using several velocities and concentration mixtures. Pin-fin tubes were used to obtain coherent results using a photographic method. The experimental setup was designed to monitor the movement of fluid through the apparatus. The velocity was varied using dampers and visibility was enhanced using dyes. Photographs were taken at 20 m/s velocities after every 20 s. and 0.1% concentration and the flooding point observed. The experimental results were verified by standard observation which showed little variation at lower velocity. For water/water-propanol mixtures, a vapor velocity of 12 m/s and concentration ratio of 0.04% was the optimal combination to achieve useful improvement in retention angle. With increase of propanol from 0% to 0.04%, the increase in retention angle was greater compared to 0.04% to 0.1%. For velocities ranging from 0 to 12 m/s, the increase in retention angle was significant. A sharp change was observed for concentration ratios ranging from 0.01% to 0.05% compared to 0.05% to 0.1%. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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25 pages, 10828 KiB

Open AccessArticle

Wind Farm Site Selection Using WAsP Tool for Application in the Tropical Region

by Ismail Kamdar, Shahid Ali, Juntakan Taweekun and Hafiz Muhammad Ali

Sustainability 2021, 13(24), 13718; https://0-doi-org.brum.beds.ac.uk/10.3390/su132413718 - 12 Dec 2021

Cited by 12 | Viewed by 4482

Abstract

Wind energy is one of the most promising renewable energy technologies worldwide; however, assessing potential sites for wind energy exploitation is a challenging task. This study presents a site suitability analysis to develop a small–scale wind farm in south–eastern Thailand. To this aim, [...] Read more.

Wind energy is one of the most promising renewable energy technologies worldwide; however, assessing potential sites for wind energy exploitation is a challenging task. This study presents a site suitability analysis to develop a small–scale wind farm in south–eastern Thailand. To this aim, the most recent available data from 2017 to 2019, recorded near the surface, at nine weather stations of the Thai Meteorological Department (TMD) were acquired. The analysis was conducted using standard wind–industry software WAsP. It was found that the mountain peaks and ridges are highly suitable for small–scale wind farm development. Nevertheless, the wind data analysis indicates that regions fall in low–to–moderate wind classes. The selected sites in south–eastern Thailand have mean wind speeds ranging from 5.1 m/s to 9.4 m/s. Moreover, annual energy production (AEP) of 102 MWh to 311 MWh could be generated using an Enercon E–18 wind turbine with a rated power of 80-kW at the hub height of 28.5 m. The Levelized Cost of Energy (LCOE) reveals that the development cost of a small–scale wind farm is lowest in the Songkhla and Yala provinces of Thailand, therefore these two locations from the investigated study region are financially most suitable. The findings could encourage researchers to further investigate low–speed wind energy mechanisms in tropical regions, and the demonstrated approach could be reused for other regions. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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17 pages, 9004 KiB

Open AccessArticle

Applying Artificial Neural Network and Response Surface Method to Forecast the Rheological Behavior of Hybrid Nano-Antifreeze Containing Graphene Oxide and Copper Oxide Nanomaterials

by Ammar A. Melaibari, Yacine Khetib, Abdullah K. Alanazi, S. Mohammad Sajadi, Mohsen Sharifpur and Goshtasp Cheraghian

Sustainability 2021, 13(20), 11505; https://0-doi-org.brum.beds.ac.uk/10.3390/su132011505 - 18 Oct 2021

Cited by 8 | Viewed by 1700

Abstract

In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second [...] Read more.

In this study, the efficacy of loading graphene oxide and copper oxide nanoparticles into ethylene glycol-water on viscosity was assessed by applying two numerical techniques. The first technique employed the response surface methodology based on the design of experiments, while in the second technique, artificial intelligence algorithms were implemented to estimate the GO-CuO/water-EG hybrid nanofluid viscosity. The nanofluid sample’s behavior at 0.1, 0.2, and 0.4 vol.% is in agreement with the Newtonian behavior of the base fluid, but loading more nanoparticles conforms with the behavior of the fluid with non-Newtonian classification. Considering the possibility of non-Newtonian behavior of nanofluid temperature, shear rate and volume fraction were effective on the target variable and were defined in the implementation of both techniques. Considering two constraints (i.e., the maximum R-square value and the minimum mean square error), the best neural network and suitable polynomial were selected. Finally, a comparison was made between the two techniques to evaluate their potential in viscosity estimation. Statistical considerations proved that the R-squared for ANN and RSM techniques could reach 0.995 and 0.944, respectively, which is an indication of the superiority of the ANN technique to the RSM one. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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12 pages, 3306 KiB

Open AccessArticle

Fabrication of Catalytic Converter with Different Materials and Comparison with Existing Materials in Addition to Analysis of Turbine Installed at the Exhaust of 4 Stroke SI Engine

by Roman Kalvin, Juntakan Taweekun, Kittinan Maliwan and Hafiz Muhammad Ali

Sustainability 2021, 13(18), 10470; https://0-doi-org.brum.beds.ac.uk/10.3390/su131810470 - 21 Sep 2021

Cited by 6 | Viewed by 2654

Abstract

Harmful pollutants (CO, NO, and unburnt hydrocarbons) coming out from the exhaust manifold of an engine must be converted into harmless gases by using catalytic converter. This field has seen vast research for increasing the conversion efficiency of pollutants by using different cheap [...] Read more.

Harmful pollutants (CO, NO, and unburnt hydrocarbons) coming out from the exhaust manifold of an engine must be converted into harmless gases by using catalytic converter. This field has seen vast research for increasing the conversion efficiency of pollutants by using different cheap metals. Nowadays, catalysts used in catalytic converter are noble metals, and they are also critical in the sense that they are not abundant on Earth. Platinum, palladium and rhodium are very expensive; hence, low-cost cars are not installed with catalytic converter, especially in third world countries. This research has been carried out to assess the catalytic activity of catalysts made from the salt/metal precursors, cerium sulphate tetra hydrate, manganese sulphate mono hydrate and copper sulphate penta hydrate that are not expensive and also less affected by the poison. Test sample catalysts were prepared through a coprecipitation method having different molar concentrations, and then tested for the conversion efficiency by applying the catalysts on ceramic plates by using flue gas analyzer. On the basis of the results, final catalysts were prepared and applied on a monolithic ceramic plate and then tested with regard to the resulting conversion rate of pollutants as compared to already installed catalytic converter. Moreover, turbine was installed in the exhaust passage to generate the power that would be utilized to run the electrical accessories of the engine. SOLIDWORKS were used for 3D CAD modeling and the flow analysis of turbine with radial inlet-axial outlet. In addition, ANSYS was used for stress-strain analysis. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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17 pages, 4570 KiB

Open AccessArticle

Predicting Parameters of Heat Transfer in a Shell and Tube Heat Exchanger Using Aluminum Oxide Nanofluid with Artificial Neural Network (ANN) and Self-Organizing Map (SOM)

by Amir Zolghadri, Heydar Maddah, Mohammad Hossein Ahmadi and Mohsen Sharifpur

Sustainability 2021, 13(16), 8824; https://0-doi-org.brum.beds.ac.uk/10.3390/su13168824 - 06 Aug 2021

Cited by 16 | Viewed by 1717

Abstract

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of [...] Read more.

This study is a model of artificial perceptron neural network including three inputs to predict the Nusselt number and energy consumption in the processing of tomato paste in a shell-and-tube heat exchanger with aluminum oxide nanofluid. The Reynolds number in the range of 150–350, temperature in the range of 70–90 K, and nanoparticle concentration in the range of 2–4% were selected as network input variables, while the corresponding Nusselt number and energy consumption were considered as the network target. The network has 3 inputs, 1 hidden layer with 22 neurons and an output layer. The SOM neural network was also used to determine the number of winner neurons. The advanced optimal artificial neural network model shows a reasonable agreement in predicting experimental data with mean square errors of 0.0023357 and 0.00011465 and correlation coefficients of 0.9994 and 0.9993 for the Nusselt number and energy consumption data set. The obtained values of e_MAX for the Nusselt number and energy consumption are 0.1114, and 0.02, respectively. Desirable results obtained for the two factors of correlation coefficient and mean square error indicate the successful prediction by artificial neural network with a topology of 3-22-2. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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41 pages, 17954 KiB

Open AccessArticle

Turbulent Flow Heat Transfer through a Circular Tube with Novel Hybrid Grooved Tape Inserts: Thermohydraulic Analysis and Prediction by Applying Machine Learning Model

by Suvanjan Bhattacharyya, Devendra Kumar Vishwakarma, Shramona Chakraborty, Rahul Roy, Alibek Issakhov and Mohsen Sharifpur

Sustainability 2021, 13(6), 3068; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063068 - 11 Mar 2021

Cited by 20 | Viewed by 2283

Abstract

The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the [...] Read more.

The present experimental work is performed to investigate the convection heat transfer (HT), pressure drop (PD), irreversibility, exergy efficiency and thermal performance for turbulent flow inside a uniformly heated circular channel fitted with novel geometry of hybrid tape. Air is taken as the working fluid and the Reynolds number is varied from 10,000 to 80,000. Hybrid tape is made up of a combination of grooved spring tape and wavy tape. The results obtained with the novel hybrid tape show significantly better performance over individual tapes. A correlation has been developed for predicting the friction factor (f) and Nusselt number (Nu) with novel hybrid tape. The results of this investigation can be used in designing heat exchangers. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is trained based on the features of experimental data, which provide an estimation of experimental output based on user-defined input parameters. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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24 pages, 5080 KiB

Open AccessArticle

Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

by Basma Souayeh, Suvanjan Bhattacharyya, Najib Hdhiri and Mir Waqas Alam

Sustainability 2021, 13(6), 3023; https://0-doi-org.brum.beds.ac.uk/10.3390/su13063023 - 10 Mar 2021

Cited by 17 | Viewed by 2002

Abstract

A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation [...] Read more.

A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation of this enhanced geometry can, therefore, be treated as a new substantial contribution in the open literature. Three different spring ratio and depth ratio has been used in this study. Increase in thermal energy transport coefficient is noticed with increase in depth ratio. Corrugated spring tape shows promising results towards heat transfer enhancement. This geometry performs significantly better (60% to 75% increase in heat duty at constant pumping power and 20% to 31% reduction in pumping power at constant heat duty) than simple spring tape. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. The results of this investigation can be used in designing heat exchangers. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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18 pages, 6736 KiB

Open AccessArticle

Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms

by Elsa Chaerun Nisa and Yean-Der Kuan

Sustainability 2021, 13(2), 744; https://0-doi-org.brum.beds.ac.uk/10.3390/su13020744 - 14 Jan 2021

Cited by 18 | Viewed by 3012

Abstract

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The [...] Read more.

Over the last few decades, total energy consumption has increased while energy resources remain limited. Energy demand management is crucial for this reason. To solve this problem, predicting and forecasting water-cooled chiller power consumption using machine learning and deep learning are presented. The prediction models adopted are thermodynamic model and multi-layer perceptron (MLP), while the time-series forecasting models adopted are MLP, one-dimensional convolutional neural network (1D-CNN), and long short-term memory (LSTM). Each group of models is compared. The best model in each group is then selected for implementation. The data were collected every minute from an academic building at one of the universities in Taiwan. The experimental result demonstrates that the best prediction model is the MLP with 0.971 of determination (R²), 0.743 kW of mean absolute error (MAE), and 1.157 kW of root mean square error (RMSE). The time-series forecasting model trained every day for three consecutive days using new data to forecast the next minute of power consumption. The best time-series forecasting model is LSTM with 0.994 of R², 0.233 kW of MAE, and 1.415 kW of RMSE. The models selected for both MLP and LSTM indicated very close predictive and forecasting values to the actual value. Full article

(This article belongs to the Special Issue Applications of Artificial Intelligence Model of Heat and Mass Transfer)

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