10 results
Search Results
Now showing 1 - 10 of 10
Article Citation - WoS: 1Citation - Scopus: 2Parametric Sensitivity Analysis and Performance Evaluation of High-Temperature Macro-Encapsulated Packed-Bed Latent Heat Storage System Operating With Transient Inlet Boundary Conditions(Mdpi, 2022) Mehrtash, Mehdi; Tari, IlkerThis paper presents the results of comprehensive numerical analyses in the performance of a packed-bed latent heat storage (PBLHS) system in terms of key performance indicators, namely charging time, charging rate, charging capacity, and charging efficiency. Numerical simulations are performed for the packed bed region using a transient two-dimensional axisymmetric model based on the local thermal non-equilibrium (LTNE) approach. The model considers the variation in the inlet temperature of the system as these storage systems are typically integrated with solar collectors that operate with intermittent solar radiation intensity. The model results are validated using the experimental data for temperature distribution throughout the bed. The simulations are carried out while changing the operating parameters such as the capsule diameter, bed porosity, inlet velocity, and the height-to-diameter aspect ratio to investigate their impact on the performance indicators. Observations indicate that low porosity, large-sized capsules, low inlet velocity, and a low height-to-diameter aspect ratio increase the charging time. In terms of achieving a high charging rate, a bed with low porosity, small-sized capsules, a high inflow velocity, and a high height-to-diameter aspect ratio is deemed advantageous. It is shown that raising the flow velocity and the height-to-diameter aspect ratio can improve the charging efficiency. These findings provide recommendations for optimizing the design and operating conditions of the system within the practical constraints.Article Citation - WoS: 1Citation - Scopus: 1Recycling Decommissioned Wind Turbine Blades for Post-Disaster Housing Applications(Mdpi, 2025) Turhan, Cihan; Durak, Murat; Saleh, Yousif Abed Saleh; Kalayci, AlperThe growing adoption of wind energy has resulted in an increasing number of decommissioned wind turbine blades, which pose significant disposal challenges due to their size, material composition, and environmental impact. Recycling these blades has thus become essential. To this aim, this study explores the potential of using recycled wind turbine blades in post-disaster housing applications and examines the feasibility of re-purposing these durable composite materials to create robust, cost-effective, and sustainable building solutions for emergency housing. A case study of a post-earthquake relief camp in Hatay, T & uuml;rkiye, affected by the 2023 earthquake, is used for analysis. First, the energy consumption of thirty traditional modular container-based post-disaster housing units is simulated with a dynamic building simulation tool. Then, the study introduces novel wind turbine blade-based housing (WTB-bH) designs developed using the same simulation tool. The energy consumption of these (WTB-bH) units is compared to that of traditional containers. The results indicate that using recycled wind turbine blades for housing not only contributes to waste reduction but also achieves 27.3% energy savings compared to conventional methods. The novelty of this study is in demonstrating the potential of recycled wind turbine blades to offer durable and resilient housing solutions in post-disaster situations and to advocate for integrating this recycling method into disaster recovery frameworks, highlighting its ability to enhance sustainability and resource efficiency in construction. Overall, the output of this study may help to present a compelling case for the innovative reuse of decommissioned wind turbine blades, providing an eco-friendly alternative to traditional waste disposal methods while addressing critical needs in post-disaster scenarios.Article Citation - WoS: 16Citation - Scopus: 20Forecasting Air Quality in Tripoli: an Evaluation of Deep Learning Models for Hourly Pm2.5 Surface Mass Concentrations(Mdpi, 2023) Esager, Marwa Winis Misbah; Unlu, Kamil DemirberkIn this article, we aimed to study the forecasting of hourly PM2.5 surface mass concentrations in the city of Tripoli, Libya. We employed three state-of-the-art deep learning models, namely long short-term memory, gated recurrent unit, and convolutional neural networks, to forecast PM2.5 levels using univariate time series methodology. Our results revealed that the convolutional neural networks model performed the best, with a coefficient of variation of 99% and a mean absolute percentage error of 0.04. These findings provide valuable insights into the use of deep learning models for forecasting PM2.5 and can inform decision-making regarding air quality management in the city of Tripoli.Article Citation - WoS: 3Citation - Scopus: 3Parametric Sensitivity Analysis and Performance Evaluation of High-Temperature Anion-Exchange Membrane Fuel Cell(Mdpi, 2022) Mehrtash, MehdiIn this paper, a three-dimensional model of a high-temperature anion-exchange membrane fuel cell (HT-AEMFC) operating at 110 degrees C is presented. All major transport phenomena along with the electrochemical reactions that occur in the cell are modeled. Since the water is exclusively in the form of steam and there is no phase transition to deal with in the cell, the water management is greatly simplified. The cell performance under various current loads is evaluated, and the results are validated against the experimental data. The cell performance is examined across a range of operating conditions, including cell temperature, inlet flow rate, and inlet relative humidity (RH). The critical link between the local distributions of species and local current densities along the channels is identified. The distribution of reactants continuously drops in the gas flow direction along the flow channels, causing a non-uniform local current distribution that becomes more pronounced at high current loads, where the rate of water generation increases. The findings show that while a higher inlet flow rate enhances the cell performance, a lower flow rate causes it to drop because of reactant depletion in the anode. The sensitivity analysis reveals that the performance of an AEMFC is highly dependent on the humidity of the gas entering the cell. While high inlet RH on the cathode side enhances the cell performance, high inlet RH on the anode side deteriorates it.Article Citation - WoS: 3Citation - Scopus: 3Shared 6mer Peptides of Human and Omicron (21k and 21l) at Sars-Cov Mutation Sites(Mdpi, 2022) Adiguzel, Yekbun; Shoenfeld, YehudaWe investigated the short sequences involving Omicron 21K and Omicron 21L variants to reveal any possible molecular mimicry-associated autoimmunity risks and changes in those. We first identified common 6mers of the viral and human protein sequences present for both the mutant (Omicron) and nonmutant (SARS-CoV-2) versions of the same viral sequence and then predicted the binding affinities of those sequences to the HLA supertype representatives. We evaluated change in the potential autoimmunity risk, through comparative assessment of the nonmutant and mutant viral sequences and their similar human peptides with common 6mers and affinities to the same HLA allele. This change is the lost and the new, or de novo, autoimmunity risk, associated with the mutations in the Omicron 21K and Omicron 21L variants. Accordingly, e.g., the affinity of virus-similar sequences of the Ig heavy chain junction regions shifted from the HLA-B*15:01 to the HLA-A*01:01 allele at the mutant sequences. Additionally, peptides of different human proteins sharing 6mers with SARS-CoV-2 proteins at the mutation sites of interest and with affinities to the HLA-B*07:02 allele, such as the respective SARS-CoV-2 sequences, were lost. Among all, any possible molecular mimicry-associated novel risk appeared to be prominent in HLA-A*24:02 and HLA-B*27:05 serotypes upon infection with Omicron 21L. Associated disease, pathway, and tissue expression data supported possible new risks for the HLA-B*27:05 and HLA-A*01:01 serotypes, while the risks for the HLA-B*07:02 serotypes could have been lost or diminished, and those for the HLA-A*03:01 serotypes could have been retained, for the individuals infected with Omicron variants under study. These are likely to affect the complications related to cross-reactions influencing the relevant HLA serotypes upon infection with Omicron 21K and Omicron 21L.Article Citation - WoS: 24Citation - Scopus: 27The Influence of Meteorological Parameters on Pm10: a Statistical Analysis of an Urban and Rural Environment in Izmir/Turkiye(Mdpi, 2023) Birim, Necmiye Gulin; Turhan, Cihan; Atalay, Ali Serdar; Gokcen Akkurt, GuldenAir pollution is a substantial menace, especially in industrialized urban zones, which affects the balance of the environment, life of vital organisms and human health. Besides the main causes of air pollution such as dense urbanization, poor quality fuels and vehicle emissions, physical environment characteristics play an important role on air quality. Therefore, it is vital to understand the relationship between the characteristics of the natural environment and air quality. This study examines the correlations between the PM10 pollutant data and meteorological parameters such as temperature (T-air), relative humidity (RH), and wind speed (WS) and direction (WD) under the European Union's Horizon 2020 project. Two different zones (Vilayetler Evi as an urban zone and Sasali Natural Life Park as a rural zone) of Izmir Province in Turkiye are used as a case study and the PM10 data is evaluated between 1 January 2017 and 31 December 2021. A one-tailed t-test is used in order to statistically determine the relationships between the PM10 pollutant data and meteorological parameters. As a further study, practical significance of the parameters is investigated via the effect size method and the results show that the RH is found to be the most influencing parameter on the PM10 for both zones, while T-air is found to be statistically non-significant.Article Citation - WoS: 13Citation - Scopus: 16Estimating the Parameters of Fitzhugh-Nagumo Neurons From Neural Spiking Data(Mdpi, 2019) Doruk, Resat Ozgur; Abosharb, LailaA theoretical and computational study on the estimation of the parameters of a single Fitzhugh-Nagumo model is presented. The difference of this work from a conventional system identification is that the measured data only consist of discrete and noisy neural spiking (spike times) data, which contain no amplitude information. The goal can be achieved by applying a maximum likelihood estimation approach where the likelihood function is derived from point process statistics. The firing rate of the neuron was assumed as a nonlinear map (logistic sigmoid) relating it to the membrane potential variable. The stimulus data were generated by a phased cosine Fourier series having fixed amplitude and frequency but a randomly shot phase (shot at each repeated trial). Various values of amplitude, stimulus component size, and sample size were applied to examine the effect of stimulus to the identification process. Results are presented in tabular and graphical forms, which also include statistical analysis (mean and standard deviation of the estimates). We also tested our model using realistic data from a previous research (H1 neurons of blowflies) and found that the estimates have a tendency to converge.Article Citation - WoS: 22Citation - Scopus: 23Understanding Corrosion Morphology of Duplex Stainless Steel Wire in Chloride Electrolyte(Mdpi, 2021) Ornek, Cem; Davut, Kemal; Kocabas, Mustafa; Bayatli, Aleyna; Urgen, MustafaThe corrosion morphology in grade 2205 duplex stainless steel wire was studied to understand the nature of pitting and the causes of the ferrite phase's selective corrosion in acidic (pH 3) NaCl solutions at 60 degrees C. It is shown that the corrosion mechanism is always pitting, which either manifests lacy cover perforation or densely arrayed selective cavities developing selectively on the ferrite phase. Pits with a lacy metal cover form in concentrated chloride solutions, whereas the ferrite phase's selective corrosion develops in diluted electrolytes, showing dependency on the chloride-ion concentration. The pit perforation is probabilistic and occurs on both austenite and ferrite grains. The lacy metal covers collapse in concentrated solutions but remain intact in diluted electrolytes. The collapse of the lacy metal cover happens due to hydrogen embrittlement. Pit evolution is deterministic and occurs selectively in the ferrite phase in light chloride solutions.Article Citation - WoS: 56Citation - Scopus: 105Windows Pe Malware Detection Using Ensemble Learning(Mdpi, 2021) Azeez, Nureni Ayofe; Odufuwa, Oluwanifise Ebunoluwa; Misra, Sanjay; Oluranti, Jonathan; Damasevicius, RobertasIn this Internet age, there are increasingly many threats to the security and safety of users daily. One of such threats is malicious software otherwise known as malware (ransomware, Trojans, viruses, etc.). The effect of this threat can lead to loss or malicious replacement of important information (such as bank account details, etc.). Malware creators have been able to bypass traditional methods of malware detection, which can be time-consuming and unreliable for unknown malware. This motivates the need for intelligent ways to detect malware, especially new malware which have not been evaluated or studied before. Machine learning provides an intelligent way to detect malware and comprises two stages: feature extraction and classification. This study suggests an ensemble learning-based method for malware detection. The base stage classification is done by a stacked ensemble of fully-connected and one-dimensional convolutional neural networks (CNNs), whereas the end-stage classification is done by a machine learning algorithm. For a meta-learner, we analyzed and compared 15 machine learning classifiers. For comparison, five machine learning algorithms were used: naive Bayes, decision tree, random forest, gradient boosting, and AdaBoosting. The results of experiments made on the Windows Portable Executable (PE) malware dataset are presented. The best results were obtained by an ensemble of seven neural networks and the ExtraTrees classifier as a final-stage classifier.Article Citation - WoS: 4Citation - Scopus: 4A Combined Experimental and Numerical Thermo-Hydrodynamic Investigation of High-Temperature Fluidized-Bed Thermal Energy Storage(Mdpi, 2022) Mehrtash, Mehdi; Karadiken, Esra Polat; Tari, IlkerThe present research describes the design, analysis, and modeling of an air-granular particle fluidized-bed system with dimensions of 0.08 m x 0.4 m x 0.08 m. The hydrodynamic and thermal experiments are designed to verify the numerical model previously created for this purpose. The gas-solid two-phase flow is described using a three-dimensional, two-fluid model based on the Eulerian-Eulerian method. The experiment is conducted, and the numerical model is updated for the new geometry while maintaining the solution parameters. Silica sand and sintered bauxite particles are employed in both experimental and numerical investigations to examine the behaviors of these particles. The hydrodynamic validity of the numerical model is established by the use of experimental findings for pressure drop and bed expansion ratio. The thermal tests are conducted with 585 K hot sand, and the temperature distribution in the bed is measured using K-type thermocouples and compared with the simulation data. Both the hydrodynamical and thermal experimental data appear to agree with the conclusions of the computational analyses. The validated model is then used to mimic the performance of the bed at elevated temperatures. The performance indicators are discussed and calculated for 973 K, demonstrating that as the temperature rises, the system efficiency increases.
