Mohamed Ouni, Fatih Selimefendigil, Besbes Hatem, Lioua Kolsi and Mohamed Omri
The purpose of this study is to analyze the impacts of combined utilization of multi-jet impinging cooling of nanofluids with magnetic field and porous layer on the cooling…
Abstract
Purpose
The purpose of this study is to analyze the impacts of combined utilization of multi-jet impinging cooling of nanofluids with magnetic field and porous layer on the cooling performance, as effective cooling with impinging jets are obtained for various energy systems, including photovoltaic panels, electronic cooling and many other convective heat transfer applications.
Design/methodology/approach
Finite element method is used to explore the magnetic field effects with the inclusion of porous layer on the cooling performance efficiency of slot nanojet impingement system. Impacts of pertinent parameters such as Reynolds number (Re between 250 and 1,000), strength of magnetic field (Ha between 0 and 30), permeability of the porous layer (Da between 0.001 and 0.1) on the cooling performance for flat and wavy surface configurations are explored.
Findings
It is observed that the average Nusselt number (Nu) rises by about 17% and 20.4% for flat and wavy configuration while temperature drop of 4 K is obtained when Re is increased to 1,000 from 250. By using magnetic field at the highest strength, the average Nu rises by about 29% and 7% for flat and wavy cases. Porous layer permeability is an effective way of controlling the cooling performance while up to 44.5% variations in the average Nu is obtained by varying its value. An optimization routine is used to achieve the highest cooling rate while the optimum parameter set is obtained as (Re, Ha, Da, γ, sx) = (1,000, 30, 0.07558, 86.28, 2.585) for flat surface and (Re, Ha, Da, γ, sx) = (1,000, 30, 0.07558, 71.85, 2.329) for wavy surface configurations.
Originality/value
In thermal systems, cooling system design is important for thermal management of various energy systems, including fuel cells, photovoltaic panels, electronic cooling and many others. Impinging jets are considered as effective way of cooling because of its ability to give higher local heat transfer coefficients. This paper offers novel control tools, such as magnetic field, installation of porous layer and hybrid nano-liquid utilization for control of cooling performance with multiple impinging jets.
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Mouna Feki, Hédia Hannachi, Moez Bou Ali, Haytem Hamrouni, Elvira Romano, Boubaker Karray and Mohamed Hammami
The purpose of this paper is to build a class model to confirm the authenticity of olives from Bi'r al Malluli, Tunisian region, in order to obtain the Designation of Origin (DO).
Abstract
Purpose
The purpose of this paper is to build a class model to confirm the authenticity of olives from Bi'r al Malluli, Tunisian region, in order to obtain the Designation of Origin (DO).
Design/methodology/approach
In total, ten orchards of Chemlali olive oil variety were chosen, in Sfax region, characterized by the same applied cultural techniques. Pomological characters of olives, fatty acids composition and organoleptic analysis of olive oil were conducted.
Findings
Results showed that the pomological characters were specific of the Chemlali variety: the olive weight ranged from 0.9 to 1.10 g in all studied orchards and the water content (WC) ranged from 41.45 to 57.68 per cent. All analysed oils showed good fatty acids balance. Chemlali olive oil contains high amounts of oleic acid and a smaller amount of linoleic acid. The oleic acid content ranged from 57.96 to 63.52 per cent according to the orchards. All oils having oleic acid higher than 55 per cent are categorized as extra virgin olive oil based on International Olive Oil Council (IOOC) Norma. Based on the organoleptic analysis, all the analysed oils were classified as an extra virgin olive oil. The principal component analysis applied separately on olive characters and fatty acids contents do not indicate any group's structure.
Originality/value
An objective approach based on pomologic, sensory and acidic composition analyses would be used to delimitate Protected Designation of Origin (PDOs) in olive oil from the Bi'r al Malluli area and better protect their markets.
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Salman Tariq, Mohamed Hussein, Roy Dong Wang and Tarek Zayed
This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular…
Abstract
Purpose
This study aims to thoroughly examine the trends and developments of crane layout planning (CLP) in the construction field and reveal future research directions for modular integrated construction (MiC).
Design/methodology/approach
Through a rigorous systematic mixed-review methodology that integrates bibliometric, scientometric and qualitative analysis, this study explored the crane layout research trend; the scientometric analysis of journal sources and keywords occurrence network; the research contributions and links between influential countries; the classification of research articles based on the type of problems and solution approaches; the qualitative analysis of existing findings and research gaps; and the future research direction for CLP in MiC.
Findings
This study found five categories under the CLP domain, namely, crane selection, crane location, integrated crane selection and location, integrated crane location and allocation of supply points and hybrid problems. The major research approaches used to solve CLP is optimization (43%), visualization (23%), decision support systems (16%), simulation (11%) and qualitative techniques (7%). The possible future research directions include artificial intelligence-based models, multi-crane locations, CLP for MiC re-use, dynamic models representing real-life scenarios and building information modeling-based virtual reality models.
Originality/value
Through a mixed-review methodology, this study provides a comprehensive analysis of problem settings and solution methods of CLP while mitigating the subjectivity of traditional review methods. Also, it presents a repertoire on CLP and illuminates future directions for seasoned researchers in the context of MiC.
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Ahmed Babeker Elhag, Ali Raza, Nabil Ben Kahla and Muhammed Arshad
The external confinement provided by the fiber-reinforced polymer (FRP) sheets leads to an improvement in the axial compressive strength (CS) and strain of reinforced concrete…
Abstract
Purpose
The external confinement provided by the fiber-reinforced polymer (FRP) sheets leads to an improvement in the axial compressive strength (CS) and strain of reinforced concrete structural members. Many studies have proposed analytical models to predict the axial CS of concrete structural members, but the predictions for the axial compressive strain still need more investigation because the previous strain models are not accurate enough. Moreover, the previous strain models were proposed using small and noisy databases using simple modeling techniques. Therefore, a rigorous approach is needed to propose a more accurate strain model and compare its predictions with the previous models.
Design/methodology/approach
The present work has endeavored to propose strain models for FRP-confined concrete members using three different techniques: analytical modeling, artificial neural network (ANN) modeling and finite element analysis (FEA) modeling based on a large database consisting of 570 sample points.
Findings
The assessment of the previous models using some statistical parameters revealed that the estimates of the newly recommended models were more accurate than the previous models. The estimates of the new models were validated using the experimental outcomes of compressive members confined with carbon-fiber-reinforced polymer (CFRP) wraps. The nonlinear FEA of the tested samples was performed using ABAQUS, and its estimates were equated with the calculations of the analytical and ANN models. The relative investigation of the estimates solidly substantiates the accuracy and applicability of the recommended analytical, ANN and FEA models for predicting the axial strain of CFRP-confined concrete compression members.
Originality/value
The research introduces innovative methods for understanding FRP confinement in concrete, presenting new models to estimate axial compressive strains. Utilizing a database of 570 experimental samples, the study employs ANNs and regression analysis to develop these models. Existing models for FRP-confined concrete's axial strains are also assessed using this database. Validation involves testing 18 cylindrical specimens confined with CFRP wraps and FE simulations using a concrete-damaged plastic (CDP) model. A comprehensive comparative analysis compares experimental results with estimates from ANNs, analytical and finite element models (FEMs), offering valuable insights and predictive tools for FRP confinement in concrete.
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Sobhan Pandit, Milan K. Mondal, Dipankar Sanyal, Nirmal K. Manna, Nirmalendu Biswas and Dipak Kumar Mandal
This study aims to undertake a comprehensive examination of heat transfer by convection in porous systems with top and bottom walls insulated and differently heated vertical walls…
Abstract
Purpose
This study aims to undertake a comprehensive examination of heat transfer by convection in porous systems with top and bottom walls insulated and differently heated vertical walls under a magnetic field. For a specific nanofluid, the study aims to bring out the effects of different segmental heating arrangements.
Design/methodology/approach
An existing in-house code based on the finite volume method has provided the numerical solution of the coupled nondimensional transport equations. Following a validation study, different explorations include the variations of Darcy–Rayleigh number (Ram = 10–104), Darcy number (Da = 10–5–10–1) segmented arrangements of heaters of identical total length, porosity index (ε = 0.1–1) and aspect ratio of the cavity (AR = 0.25–2) under Hartmann number (Ha = 10–70) and volume fraction of φ = 0.1% for the nanoparticles. In the analysis, there are major roles of the streamlines, isotherms and heatlines on the vertical mid-plane of the cavity and the profiles of the flow velocity and temperature on the central line of the section.
Findings
The finding of a monotonic rise in the heat transfer rate with an increase in Ram from 10 to 104 has prompted a further comparison of the rate at Ram equal to 104 with the total length of the heaters kept constant in all the cases. With respect to uniform heating of one entire wall, the study reveals a significant advantage of 246% rate enhancement from two equal heater segments placed centrally on opposite walls. This rate has emerged higher by 82% and 249%, respectively, with both the segments placed at the top and one at the bottom and one at the top. An increase in the number of centrally arranged heaters on each wall from one to five has yielded 286% rate enhancement. Changes in the ratio of the cavity height-to-length from 1.0 to 0.2 and 2 cause the rate to decrease by 50% and increase by 21%, respectively.
Research limitations/implications
Further research with additional parameters, geometries and configurations will consolidate the understanding. Experimental validation can complement the numerical simulations presented in this study.
Originality/value
This research contributes to the field by integrating segmented heating, magnetic fields and hybrid nanofluid in a porous flow domain, addressing existing research gaps. The findings provide valuable insights for enhancing thermal performance, and controlling heat transfer locally, and have implications for medical treatments, thermal management systems and related fields. The research opens up new possibilities for precise thermal management and offers directions for future investigations.
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Ji-Myong Kim, Sang-Guk Yum, Manik Das Adhikari and Junseo Bae
This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that…
Abstract
Purpose
This study proposes a deep learning algorithm-based model to predict the repair and maintenance costs of apartment buildings, by collecting repair and maintenance cost data that were incurred in an actual apartment complex. More specifically, a long short-term memory (LSTM) algorithm was adopted to develop the prediction model, while the robustness of the model was verified by recurrent neural networks (RNN) and gated recurrent units (GRU) models.
Design/methodology/approach
Repair and maintenance cost data incurred in actual apartment complexes is collected, along with various input variables, such as repair and maintenance timing (calendar year), usage types, building ages, temperature, precipitation, wind speed, humidity and solar radiation. Then, the LSTM algorithm is employed to predict the costs, while two other learning models (RNN and GRU) are taught to validate the robustness of the LSTM model based on R-squared values, mean absolute errors and root mean square errors.
Findings
The LSTM model’s learning is more accurate and reliable to predict repair and maintenance costs of apartment complex, compared to the RNN and GRU models’ learning performance. The proposed model provides a valuable tool that can contribute to mitigating financial management risks and reducing losses in forthcoming apartment construction projects.
Originality/value
Gathering a real-world high-quality data set of apartment’s repair and maintenance costs, this study provides a highly reliable prediction model that can respond to various scenarios to help apartment complex managers plan resources more efficiently, and manage the budget required for repair and maintenance more effectively.
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Ahmet Esat Suzer and Aziz Kaba
The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey.
Abstract
Purpose
The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey.
Design methodology approach
Three different probability distributions, namely, Inverse Gaussian (IG), widely used two-parameter Weibull and Rayleigh distributions in the literature, are used to represent wind regime and characteristics of the runway. The parameters of each distribution are estimated by the pattern search (PS)-based heuristic algorithm. The results are compared with the other three methods-based numerical computation, including maximum-likelihood method, moment method (MoM) and power density method, respectively. To evaluate the fitting performance of the proposed method, several statistical goodness tests including the mostly used root mean square error (RMSE) and chi-squared (X2) are conducted.
Findings
In the light of the statistical goodness tests, the results of the IG-based PS attain better performance than the classical Weibull and Rayleigh functions. Both the RMSE and X2 values achieved by the IG-based PS method lower than that of Weibull and Rayleigh distributions. It exhibits a better fitting performance with 0.0074 for RMSE and 0.58 × 10−4 for X2 for probability density function (PDF) in 2012 and with RMSE of 0.0084 and X2 of 0.74 × 10−4 for PDF in 2013. As regard the cumulative density function of the measured wind data, the best results are found to be Weibull-based PS with RMSE of 0.0175 and X2 of 3.25 × 10−4 in 2012. However, Weibull-based MoM shows more excellent ability in 2013, with RMSE of 0.0166 and X2 of 2.94 × 10−4. Consequently, it is considered that the results of this study confirm that IG-based PS with the lowest error value can a good choice to model more accurately and characterize the wind speed profile of the airport.
Practical implications
This paper presents a realistic point of view regarding the wind regime and characteristics of an airport. This study may cast the light on researchers, policymakers, policy analysts and airport designers intending to investigate the wind profile of a runway at the airport in the world and also provide a significant pathway on how to determine the wind distribution of the runway.
Originality value
Instead of the well-known Weibull distribution for the representing of wind distribution in the literature, in this paper, IG distribution is used. Furthermore, the suitability of IG to represent the wind distribution is validated when compared with two-parameter Weibull and Rayleigh distributions. Besides, the performance and efficiency of PS have been evaluated by comparing it with other methods.