Browsing by Publisher "Institute for Ionics"

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    Experimental Analysis of Melting Behavior of Capric Acid (CA)–Stearic Acid (SA) Eutectic Mixture and its 3D Numerical Solution of Natural Convection in a Cup
    (Institute for Ionics, 2022) Coşanay H.; Selimefendigil F.; Öztop H.F.; Sarı A.
    Computational studies were performed to investigate the melting process in a cubical closed space. Capric acid (CA)–stearic acid (SA) eutectic mixture is chosen as a phase change material (PCM). Some thermo-physical properties such as melting temperature, latent heat of fusion, specific heat capacity, thermal conductivity, thermal expansion coefficient, density and viscosity of CA-SA eutectic mixture prepared as PCM were measured. Considering these measured properties, the melting behavior of the prepared eutectic PCM was simulated numerically with finite volume method in a three-dimensional cavity. Non-isothermal heating conditions throughout phase change process of the PCM are considered in the numerical modeling study. It is shown that temperature difference has a significant impact on the melting, while its behavior changes for various cross sections. It is observed that the melting does not change after 80 min for the studied PCM. Significant variations for the melting behavior are observed between 2D and 3D configurations. Melt fraction is only 1.20% higher in 3D case as compared to 2D case at t = 130 min and Gr = 1.8 × 105, while this value is 1.08% at Gr = 3.6 × 104. © 2022, King Fahd University of Petroleum & Minerals.
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    The Experimental Assessment of Deformation and Springback Behavior for Additive Manufactured Ti6Al4V
    (Institute for Ionics, 2023) Aydın K.; Karamolla M.; Karaağaç İ.; Kabakçı M.O.; Doğan H.
    In this study, heat treatment was applied to Ti6Al4V materials produced by the selective laser melting (SLM) method at 550 ℃, 800 ℃, 950 ℃, and 1080 ℃ temperatures, and the effect of this process on microstructure and mechanical properties was also investigated. Moreover, the parts produced with the additive manufacturing technique can be exposed to many loads at the place of use. Therefore, to determine the resistance of the materials against bending, the V bending process was applied to the materials in 6-, 7.5-, and 9-degree dies. XRD and SEM investigated the microstructure and optical microscope changes, and the mechanical properties were examined by uniaxial tensile test and microhardness analysis. In the test samples, pore and unmelted powder particles due to production in the microstructure were observed, and it was observed that the grain sizes increased with the increase in the heat treatment temperature. It was observed that samples HT550, HT800, and HT950 were shaped without breaking in the bending process in the 6-degree die, and all the samples were broken in the experiments performed in the 7.5- and 9-degree die. © 2023, King Fahd University of Petroleum & Minerals.
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    Finite element-based analysis of optimally designed steel plane frames
    (Institute for Ionics, 2023) Üstüner B.; Doğan E.
    In this paper, a three-story two-span steel frame and a five-story irregular steel frame models are examined. Hunting Search algorithm (HuS), particle swarm algorithm (PSO), and Aquila algorithm (AO) are used to determine suitable frame sections. The performances of the algorithms are investigated with the welded beam design problem and the three-truss problems. The work consists of two stages as optimization and solution with finite element method. The aim of this study is to determine the frame section within the constraints to have a minimum weight. The models were created using the sections determined because of the optimizations are performed with finite element analysis. In steel frame structures, the provisions of the AISC-LRFD practice code are used. The analysis is done with the ABAQUS CAE program, which is one of the finite element methods. Frames with optimum sections are drawn and analyzed in the program. As a result of the analysis, the displacement and stress values are shown. These all values are within limits. PSO and HuS algorithms give the same results. The worst result is taken from the AO algorithm. © 2023, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    Design and synthesis of benzimidazole derivatives as apoptosis-inducing agents by targeting Bcl-2 protein
    (Institute for Ionics, 2023) Ilhan S.; Çamli Pulat Ç.; Oguz F.; Bektaş H.; Menteşe E.; Atmaca H.
    Bcl-2, an anti-apoptotic protein, is a well-known and appealing cancer therapy target. Novel series of benzimidazole derivatives were synthesized and tested for their activity as Bcl-2 inhibitors on T98G glioblastoma, PC3 prostate, MCF-7 breast, and H69AR lung cancer cells. MTT assay was used to evaluate the cytotoxic effect. PI Annexin V Apoptosis Detection Kit was used to detect apoptosis. Expression levels of the Bcl-2 protein were examined by the Western blot analysis and qRT-PCR. All synthesized benzimidazole derivatives exhibited a cytotoxic effect on cancer cells with IC50 values in the range of 25.2–88.2 µg/mL. Among all derivatives, compounds C1 and D1 demonstrated a higher cytotoxic effect on cancer cells with IC50 values < 50 µg/mL, while a lower cytotoxic effect against human embryonic kidney cells with IC50 values of > 100 µg/mL. C1 and D1 caused a significant increase in the percentage of apoptotic cells in all types of cancer cell cells and both Bcl-2 mRNA and protein levels were significantly reduced. These results suggest that the novel benzimidazole derivatives may be candidates for apoptosis-inducing agents in cancer treatment by targeting anti-Bcl-2 proteins in cancer cells. Graphical abstract: [Figure not available: see fulltext.]. © 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.
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    Estimation of Natural Frequencies of Pipe–Fluid–Mass System by Using Causal Discovery Algorithm
    (Institute for Ionics, 2023) Dagli B.Y.; Ergut A.; Özyüksel Çiftçioğlu A.
    This paper employs a novel approach to investigating the dynamic behavior of a pipe conveying fluid and the relationship between the variables that influence it, based on causal inference. The pipe is modeled as a beam with Rayleigh beam theory and Hamilton’s variation principle is demonstrated to obtain the equation of motion. Concentrated mass at various locations is introduced using the Dirac delta function. The fluid in the pipe has no compression properties and no viscosity. The non-dimensional equations of motion of the pipe–fluid–mass system are achieved by using the approach of the fluid–structure interaction problem. The non-dimensional partial differential equations of motion are converted into matrix equations and the values of natural frequencies are obtained by using the Finite Differences Method. The relationship between the variables is investigated by causal discovery using the produced natural vibration frequencies dataset. Moreover, the Bayesian Network's probability distribution is fitted to the discretized data using the structural model created through causal discovery, resulting in trustworthy predictions without the need for sophisticated analysis. The findings highlighted that the proposed causal discovery can be an alternative practical way for real-time applications of pipe conveying fluid systems. © 2022, King Fahd University of Petroleum & Minerals.
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    A New Lithium Polymer Battery Dataset with Different Discharge Levels: SOC Estimation of Lithium Polymer Batteries with Different Convolutional Neural Network Models
    (Institute for Ionics, 2023) Taş G.; Uysal A.; Bal C.
    In this study, a new dataset was created for use to estimate the state of charge (SOC) of lithium polymer batteries. A new experimental system was created to obtain the dataset by measuring the current, voltage, and temperature parameters of lithium polymer batteries. A convolutional neural network (CNN)-based deep learning model was used as the SOC prediction method. The effect of both batch size and dense network hyperparameter value on total parameter and deep learning error metric values for CNN-based lithium polymer battery SOC estimation is discussed. The proposed method, unlike deep learning models that require a high processing load in electronic cards, has provided remarkable results by being determined according to four different dense networks and two different batch size values. The proposed model has been obtained by performing experiments on optimizer, learning rate, dense network, and batch size values while determining the appropriate parameters to make successful predictions. The success of the CNN models was compared by conducting deep learning training on a computer with an Nvidia Gtx 1060 graphics card running the Ubuntu operating system. Adadelta optimizer achieved R2 0.977262 prediction success with learning rate 10–2, batch size 5 × 102, dense 105 hyperparameter values. According to the results of the experiment, it was concluded that in the CNN deep learning method, large dense layers and small batch size values created less error in SOC estimation. © 2023, King Fahd University of Petroleum & Minerals.
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    The Performance Analysis of Different Water Indices and Algorithms Using Sentinel-2 and Landsat-8 Images in Determining Water Surface: Demirkopru Dam Case Study
    (Institute for Ionics, 2023) Yilmaz O.S.; Gulgen F.; Balik Sanli F.; Ates A.M.
    In this study, the most appropriate algorithm and water index to determine the boundaries of the dam water surface using remote sensing (RS) techniques were investigated. Water surface boundaries of Demirkopru Dam were determined using Sentinel-2 L2A (MSI) and Landsat-8 (OLI) satellite images. Demirkopru Dam was chosen as the study area as it is suitable for floating photovoltaic (FPV) solar power plant installation. Normalized difference water index (NDWI) and modified NDWI indices were used to determine the water surface boundaries of the dam. Thirty-six classification results were obtained using K-means, maximum likelihood classification (MLC), and random forest (RF) algorithms. The best classification accuracies of the produced maps have been calculated as 80.3%, 73.1%, and 73.2% by RF, MLC, and K-means, respectively. In addition, the water coastlines determined by classifications were compared with the continuously operating reference station (CORS-TR) data in a local area by calculating the root-mean-square error (RMSE). Compared with the CORS-TR measurements of the dam coastline obtained from the images classified by the RF algorithm, the minimum RMSE values were calculated as 13.8 m and 10.1 m for Landsat and Sentinel images, respectively. While the minimum RMSE value for coastlines obtained with various layer stacks of Landsat images classified by the MLC algorithm is 36.7 m, it could not be calculated in Sentinel images due to poorer classification results. For the coastlines obtained from the images classified by the K-means algorithm, the minimum RMSE values were calculated as 14.5 m and 9.6 m for Landsat and Sentinel images, respectively. According to the comparisons based on classification accuracy and CORS-TR measurements, it is concluded that the RF algorithm performs better than others for the dam water surface. Moreover, it was determined that the NDWI presented better results when the water level was the lowest for Demirkopru Dam. Also, in this study, the MLC algorithm has better results in detecting water surfaces using Landsat images. It was concluded that the K-means algorithm is also very effective in water surface detection. In this study, various water extraction indices, algorithms and free Landsat and Sentinel images were used to extract the water surface in a selected reservoir for the FPV installation. This study guides a series of algorithms and indexes used to detect water surfaces. In addition, it has been shown that the use of RS techniques, which are more practical than classical approaches in determining water boundaries, will be more effective in planning and design in terms of engineers, investors and various organizations who will realize the FPV installation. © 2023, King Fahd University of Petroleum & Minerals.
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    Is the function of the core muscles affected during pregnancy?
    (Institute for Ionics, 2023) Çiçek S.; Çeliker Tosun Ö.; Parlas M.; Bilgiç D.; Yavuz O.; Kurt S.; Başer Seçer M.; Tosun G.
    Introduction and hypothesis: The aim of our study is to examine the changes in core muscle functions during pregnancy. Methods: Our study was carried out in 67 primigravida pregnant women. Superficial electromyography (EMG) and non-invasive 2D/3D ultrasonography (USG) were used to evaluate core muscle (diaphragm, transversus abdominus [TA], internal oblique [IO]–external oblique [EO] muscles, pelvic floor muscles, multifidus) function during pregnancy. Pelvic floor muscle strength was also measured by a digital palpation method (PERFECT system). USG was used to measure expected fetal weight and the diastasis recti (DR) distance. Mann–Whitney U test was used to show changes in trimesters in the core muscles, and Spearman correlation analysis was used to determine the relationship. Results: In the third trimester, an nonsignificant increase in EMG parameters was observed in all of the core muscles. Although a statistically significant decrease was observed in muscle thickness values measured by EO and IO USG in the third trimester, DR was found to increase at all levels (p < 0.005). When we evaluated both trimesters and all pregnant women together, no relationship was found between all core muscles and pelvic floor muscles in the data evaluated by EMG and USG. We found a negative correlation in USG values between fetal weight and IO and the upper part of the rectus abdominus muscle, and a positive correlation between the EMG data of the EO and rectus abdominus muscles. Conclusions: In women, the coactivation relationship between the core muscles may disappear during pregnancy. As the trimesters progress during pregnancy, a decrease in thickness and an increase in muscle activity can be observed in the core muscles. Pregnant women can be given exercise training for core muscles for protection in both the prenatal and postnatal periods. But more research needs to be done. © 2023, The International Urogynecological Association.
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    Storage location assignment of steel coils in a manufacturing company: an integer linear programming model and a greedy randomized adaptive search procedure
    (Institute for Ionics, 2023) Edis E.B.; Uzun Araz O.; Eski O.; Sancar Edis R.
    Warehouse operations have a significant role to survive in today’s competitive world. Hence, companies introduce various solutions to improve efficiency of warehouses which is mainly affected by the performance of storage operations. This study deals with a real-life storage location and assignment problem encountered in a fastener company where several orders consisting steel coils are to be assigned into storage areas. Three individual objective functions; minimizing the number of lanes to be used, minimizing area usage, and maximizing volume utilization are considered. For the investigated problem, first, an integer linear programming (ILP) model is developed. Then, a greedy randomized adaptive search procedure (GRASP) which provides quick and efficient solutions is proposed. The proposed methods are applied to the real problem case and the results are compared with the current storage assignment. Moreover, through an extensive computational study, the performances of proposed methods are evaluated on a set of test problems with different range of characteristics. The computational results show that the ILP model proves optimality in most of the problem instances within reasonable computation times, while the GRASP gives quick solutions with small optimality gaps. © 2022, The Author(s) under exclusive licence to Sociedad de Estadística e Investigación Operativa.
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    Derivation of Governing Equations by Using Vector Approach and Comparison of Analytical Solutions of Post-buckling Behaviors of Transverse Functionally Graded Shear Deformable Beam Theories
    (Institute for Ionics, 2023) Sinir B.G.
    In this study, the post-buckling behavior of a transverse functionally graded beam is investigated. Euler–Bernoulli beam theory (EBT) and shear deformable beam theories are taken into account in deriving the mathematical models of the beam using the vector approach. Timoshenko theory (TBT) and six different higher order beam theories (HOBT), namely Reddy, Touratier, Soldatos, Karma, Akavcı and Violet, are considered as shear deformable beam theories. It has been shown that mathematical models of shear deformable beam theories can be obtained using the vector approach. There are two different models developed for shear deformable beam theories depending on normal and shear forces and moment. It is found that the model which is named as Model 2 in this study yield inappropriate results. The functionally graded materials are characterized by using power law functions. The non-dimensional integro-non-linear differential equations system is solved analytically. The critical load values calculated for EBT, TBT and HOBT theories depending on different material conditions and slenderness ratio values are presented in tables. In addition, pitchfork bifurcation diagrams are drawn showing the post-buckling behavior of the beam. In addition, the regions where the examined beam theories are valid depending on the slenderness coefficient are shown. © 2022, King Fahd University of Petroleum & Minerals.
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    Usage of Graphene-Doped Tin Oxide Hybrid Nanocomposites in Compressor and Electromagnetic Modeling for Single-Phase Compressor Motor
    (Institute for Ionics, 2023) Akkaya M.
    In recent years, the use of hybrid nanolubricants in cooling systems has become necessary due to their improved heat transfer properties. In this study, hybrid graphene oxide (GO)-doped tin oxide (SnO2) nanoparticles were synthesized by a simple chemical precipitation method to produce a nanolubricant. A mass fraction of 0.75% hybrid nanoparticles in polyol ester (POE) was used in the preparation of the nanolubricant. In the synthesis of hybrid nanoparticles, GO nanoparticles were doped with SnO2 at mass fraction of 1%, 2% and 3%. Single and hybrid nanolubricants were used to determine the performance values required for compressor operation. According to the results, when hybrid nanoparticles obtained by doping 2% doped GO nanoparticles were used, the power required for compressor operation decreased by 13.47% compared to pure POE. In addition, electromagnetic modeling of the single-phase auxiliary compressor motor was performed using finite element analysis software. For the hermetic reciprocating compressor, the electric current, power dissipation and magnetic field data were analyzed. © 2022, King Fahd University of Petroleum & Minerals.