|
Yazar adı soyadı |
Yayının adı |
APA kaynak gösterimi |
Yayın özeti |
|
İsrafil KARADÖL |
Meteorolojik Veriler Kullanilarak Rüzgâr Enerjisi Üretiminin Farkli Makine Öğrenmesi Yöntemleri İle Tahmin Edilmesi |
(Karadöl, 2025a) |
Yapılan bu çalışmanın birinci amacı İzmir meteorolojik verileri ile rüzgâr enerji santraller (RES) üretimlerini kullanarak farklı makine öğrenmesi yöntemlerinin kısa vadeli RES üretim tahmin performansı incelemektir. İkinci amacı ise aynı il sınırları içeresindeki farklı RES üretimlerine (farklı hedef veri seti) göre makine öğrenmesi yöntemlerinin performansını değerlendirmektir. Çalışmada kullanılan meteorolojik veriler; nem, bulutluluk, rüzgâr yönü, rüzgâr hızı, radyasyon, toprak sıcaklığı, hava sıcaklığı ve yağış miktarıdır. RES üretim verileri ise 23MW, 20MW ve 26MW’lık tesis üretim verileridir. Bu çalışmada RES üretim tahmini yapmak amacıyla doğrusal regresyon (DR), polinomal regresyon (PR), karar ağacı (KA), rastgele orman (RO), destek vektör makinası (DVM) ve aşırı gradient artırma (XGBoost) yöntemleri kullanılmıştır. Bu yöntemlerin farklı tesis üretimleri üzerindeki tahmin performansını değerlendirmek amacıyla korelasyon katsayısı karesi (R2), ortalama mutlak hata (mean absolute error-MAE) ve hata kareleri ortalaması (mean square error-MSE) metrikleri hesaplanmıştır. Elde edilen metrik sonuçlarına göre RO’nin üç tesis üretiminde de en iyi tahmin performansı sergilediği sonucuna ulaşılmıştır. |
|
İsrafil KARADÖL |
Location Choice In Solar Power Plants By Applying Meteorological Data To Multi-Criteria Decision-Making Method |
(Karadöl & Yıldırım, 2025) |
This study aims to determine the optimum generation locations for new solar power plants by evaluating meteorological data according to analytical hierarchy process (AHP). For this purpose, meteorological data for 2021 from three different provinces in Turkey were evaluated using AHP in order to determine which province has the best solar power plant energy potential. To test the accuracy of the obtained results, various scenarios were analyzed based on annual and seasonal data, and then compared against actual plant production data. Comparisons were made by taking into account both AHP scores and average plant production data. The AHP scores obtained for Kilis, Çanakkale, and Bursa provinces were 0.63, 0.38, and 0.16, respectively, whilst the annual average per-unit production of the plants were 0.21, 0.15, and 0.06, respectively. In both the average of real production data and AHP score results, Kilis was determined as the province with the highest potential. All these applications were realized in the same way according to seasonal periods. The AHP score for Kilis province, which was determined as the best location across all four seasons, was determined as 0.55, 0.66, 0.75, and 0.71 for spring, summer, autumn, and winter, respectively. |
|
İsrafil KARADÖL |
Çok Kriterli Karar Verme Metodu Kullanılarak Biyogaz Üretiminde Kullanılacak En İyi Enerji Bitkisinin Belirlenmesi Ve Belirlenen Bu Enerji Bitkisinin Türkiye Ölçeğindeki Enerji Potansiyelinin Hesaplanması |
(Yıldırım & Karadöl, 2025) |
Türkiye yüz ölçümünün yaklaşık üçte birinde buğday, arpa, mısır, ayçiçeği ve pamuk gibi önemli enerji bitkileri ekilmektedir. Bu ürünler enerji üretiminde değerlendirildiğinde yüksek biyogaz potansiyeline sahiptir. Bu potansiyelin tespitine yönelik hem deneysel hem de teorik birçok çalışma yapılmıştır. Bu çalışmalar yapılırken bazı kabuller yapılmakta ve birtakım değişkenler göz ardı edilmektedir. Bu nedenle enerji bitkilerinin biyogaz potansiyelleri belirlenirken kriterler kullanılarak yapılan hesaplamalarla doğru sonuçlar elde edilebilmesi mümkündür. Bu çalışmanın amacı, Türkiye ölçeğinde biyogaz potansiyeli en yüksek olan enerji bitkisinin Analitik Hiyerarşi Süreci (AHS) ile belirlenmesidir. Analitik hiyerarşi sürecinde kriterler “1 m3 metan (CH4) üretilebilmesi için gerekli atık miktarı”, “Atıkların Satış Fiyatı”, “Bitkilerin Türkiye genelinde Ekili Oldukları Alan”, “Enerji Verimliliği” ve “Ürüne Özgü Değerlendirilebilir Atık Miktarı” olarak belirlenmiştir. Çalışmanın alternatifleri ise “Pamuk”, “Ayçiçeği”, “Buğday”, “Arpa” ve “Mısır” olarak seçilmiştir. Yapılan bu çalışma sonucunda 0,374 puan ile Buğday Samanı en iyi alternatif olarak belirlenmiştir. Bu sonuçlara göre Türkiye’deki en iyi enerji bitkisi buğday samanı olarak tanımlanmıştır. Bu enerji bitkisinin atıklarının (saman) Türkiye ölçeğindeki toplam biyogaz potansiyeli 156.106 m3 ve bu biyogazın enerji değeri 733,2.106 kWh/yıl olarak hesaplanmıştır. |
|
İsrafil KARADÖL |
Rüzgâr Enerji Santral Üretim Tahmininde Kullanılan Meteorolojik Veriler için En Uygun İçsel Mod Fonksiyonunun Belirlenmesi: İzmir Örneği |
(Karadöl, 2025b) |
Yapılan bu çalışmanın amacı rüzgâr enerji santral (RES) üretimlerinin uzun kısa dönem bellek (LSTM) modeli girişi parametreleri olarak kullanılan meteorolojik veriler için en uygun içsel mod fonksiyonlarının belirlenmesidir. Bu amaçla ilk olarak İzmir ilinin 2022 yılı meteorolojik verileri ve RES üretim verileri elde edilmiştir. Elde edilen meteorolojik veriler Ampirik Mod Ayrıştırma metodu kullanılarak 8 farklı içsel mod fonksiyonuna (İMF) dönüştürülmüştür. 8 farklı meteorolojik İMF’ler LSTM modelinde giriş olarak kullanılarak 2 saat sonraki RES üretimlerininim tahmin edilmesi amaçlanmıştır. LSTM modeli ile elde edilen RES üretim tahminlerinin performansını değerlendirmek amacıyla regresyon analizi (R2), ortalama mutlak hata (MAE), ortalama karesel hata (MSE) ve ortalama karesel hata kökü (RMSE) metrikleri kullanılmıştır. Bu metriklere göre test veri seti ile LSTM modeli kullanılarak en iyi RES üretim tahmini İMF1-İMF5 içsel mod fonksiyonun da gerçekleştirilmiştir. İMF1-İMF5 giriş verilerine göre MAE, MSE, RMSE ve R2 performans metrikleri sırasıyla 0.079, 0.014, 0.119 ve 0.848 olarak hesaplanmıştır. Test veri seti üzerinden ham veri ile İMF1-İMF5 arasındaki MAE, MSE, RMSE ve R2 performans metriklerini karşılaştırdığımızda, ham veriye göre MAE, MSE ve RMSE metrikleri sırasıyla %56, %74 ve %49 azalmıştır. R2 ise %204 artmıştır. Elde edilen tüm bu sonuçlar meteorolojik veriler ile LSTM ağ mimarisinde RES üretim tahmini için en uygun içsel mod fonksiyonunun İMF1-İMF5 olduğunu göstermektedir. |
Chewing simulation parameters and mechanical properties of composites as used in dental bio-materials
Yilmaz EÇ. Chewing simulation parameters and mechanical properties of composites as used in dental bio-materials. Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine. 2025;239(11-12):1087-1097. doi:10.1177/09544119251386242
Abstract
In recent years, various implant and filler materials have been used depending on the necessity of application in different parts of the human body. The biomaterial used in the living body must have adequate mechanical, esthetic, and chemical behavior throughout the treatment process. For this reason, researchers are developing many test methods to predict the behavior of biomaterials placed in the human body over time. This study aims to analyze the chewing simulation test methods and the mechanical behavior of composite materials used as filler biomaterials in the treatment process in recent years. Also, in vitro and 3D finite element analysis methods of composite materials with different filler structures were evaluated for the chewing simulation test process. Biomaterials implanted in the human body can be subjected to continuous and complex damage mechanisms. For this reason, the ability to model the behavior of the chewing simulation test method parameters performed in the laboratory environment on living tissue is important. In addition, the inadequate mechanical behavior of the composite biomaterial may lead to unsatisfactory treatment processes. Overall, understanding the fatigue and wear behavior of composite materials during chewing plays an important role in predicting in vivo results. This result is seen as an important key in improving the mechanical and esthetic behavior of composite materials over time.
Automated Detection and Classification of Newborn Crying with Machine Learning
Özdemi̇r, S., & Yilmaz, E. Ç. (2025). Automated Detection and Classification of Newborn Crying with Machine Learning. Journal of Voice. https://doi.org/https://doi.org/10.1016/j.jvoice.2025.10.041
This study aims to predict the crying behaviors and needs of newborn babies using machine learning (ML). This study also aims to support perceptual analysis with a fully ML method to evaluate the shape of newborn crying in a time band. Real-time data were obtained from 32 term newborns in stable medical condition in the neonatal intensive care unit, collected over 12-hour periods, and ML was used to estimate the needs associated with their crying behavior. In each recording, crying units are detected, even if they are long-lasting, and their classification is performed according to six basic behavioral patterns (resting, hungry, sleepy, pain, burp, and distress). While determining these behaviors, noisy signals other than the target signals are filtered from the system, contributing to the acceleration of the analysis process. This method is tested on synthesized signals and applied to recordings from selected newborns. The audio signals received from the newborn baby are transformed into a spectrogram image using the short-time Fourier transform technique. In the latter, the deep convolutional neural network technique takes the spectrogram images as input, and the baby's behavior is obtained from the convolutional neural network and passed to the support vector machine classifier. The results obtained within the scope of this study were compared independently of the recorded voice analysis and the environment that meets the needs of the newborn baby, and approximately 91,856% similarity was obtained through cry unit band time. The system designed in this study is contactless and inexpensive, making it quite suitable for routine clinical practice and may contribute to health professionals' ability to meet their babies' needs.
Investigation of Bruxism wear behavior of titanium alloy biomaterials; experimental and 3D finite element simulation
Yilmaz, E. Ç. (2025). Investigation of Bruxism wear behavior of titanium alloy biomaterials; experimental and 3D finite element simulation. Computer Methods in Biomechanics and Biomedical Engineering, 28(11), 1771–1782. https://doi.org/10.1080/10255842.2024.2339476
Bruxism can be defined as the process of direct contact with teeth and dental materials with an involuntary jaw-tightening movement. In this process, teeth and dental materials can be exposed to various damage mechanisms. This study aims to realize the mechanism of bruxism with finite element analysis and in vitro rotating chewing movement analysis. Within the scope of the study, cp-Ti, Ti-5Zr, and Ti-5Ta materials were subjected to wear tests in the finite element analysis and in vitro rotating chewing movement method under the determined Bruxism chewing test conditions. Test specimens with cylindrical geometry were exposed to a direct every-contact wear mechanism for 30 s under 150 N bruxism chewing bite force. The bruxism chewing cycle continued for 300 min at a frequency of 2 Hz. Microanalysis of the wear surfaces of the samples after the experimental study was carried out with Scanning Electron Microscopy. The results obtained within the scope of this study showed that the Bruxism wear resistance increased by adding zirconium and tantalum to pure titanium material. This result shows that pure titanium material, which is known to have poor wear resistance, can be improved with Zr and Ta alloys. It is clinically important that the success rate in the treatment process increases with the increase in wear resistance. However, the micro-cracks observed in the microstructure may have occurred in the sub-surface, which is a show of the fatigue wear mechanism.
Effect of Antagonist Material Geometry on Titanium Biomaterials Two-Body Wear Behavior: Finite Element Analysis Study
Yılmaz, E. Ç. (2025). Effect of Antagonist Material Geometry on Titanium Biomaterials Two-Body Wear Behavior: Finite Element Analysis Study. International Journal of Innovative Research and Reviews, 9(1), 22-27.
In recent years, the Finite Element Analysis method has been preferred due to its many advantages such as mathematical modeling, short time, and more experimental parameters. This study aimed at finite element analysis of two body wear behavior titanium-based biomaterial under chewing test procedures. Pure titanium test material was subjected to a 6 mm cylinder and 6 mm length square geometry antagonist abrasive material, 50 N bite force, 2 Hz chewing frequency, 0.7 mm lower jaw movement, finite element analysis chewing process. As a result of this study, force distributions occurred in the wear area of the test material in the chewing test mechanisms performed with both antagonist abrasive materials. However, the chewing bite load distribution from the cylindrical antagonist material showed a more homogeneous behavior compared to the square antagonist material. Additionally, stress concentrations were observed in certain regions of the square antagonist abrasive material. This may cause the test material to suffer from excessive wear and volume loss and damage may occur due to different deformation mechanisms.
Dynamic thermal cycle ambient simulation under mouth motion; a finite element analysis
Yılmaz, E. Ç. (2025). Dynamic thermal cycle ambient simulation under mouth motion; a finite element analysis. NanoEra, 5(1), 16-21. https://doi.org/10.5281/zenodo.15729095
The aim of this study is to analyze the experimentally established thermal cycle test experiment using the Finite Element Analysis (FEA) method. Within the scope of this study, the thermal-mechanical behavior of the pure titanium test material at minimum and maximum temperature environments was analyzed in the simulation environment occurred at 1/1 scale with the experimentally established parameters. Pure titanium test materials that different geometry were kept at 5°C temperature for 30 seconds and then exposed to 65°C environment during 2 seconds change period. Thus, 1 thermal cycle was completed under mouth motion simulation. The results obtained showed that the temperature distribution in the circular test sample exhibited a more homogeneous distribution behavior than the square test sample. This result reveals the importance of the geometric structure of the test sample in the experimental environment conditions. Therefore, the use of a circular test sample in in vitro laboratory thermal cycling experiments will increase the accuracy of the results obtained under mouth motion simulation tests. As a result, the results obtained in this study are expected to mathematically guide the selected parameters in in vitro and in vivo studies.
Analyzing the Effect of Thermal Change on Test Biomaterials in Chewing Test Experiments (Letter to Editor)
Yilmaz, Efe Çetin. Analyzing the Effect of Thermal Change on Test Biomaterials in Chewing Test Experiments. Journal of Preventive, Diagnostic and Treatment Strategies in Medicine 4(2):p 155-156, Apr–Jun 2025. | DOI: 10.4103/jpdtsm.jpdtsm_52_25
The human body has a complex and constantly changing structure. For this reason, materials preferred as biomaterials can be exposed to damage mechanisms in continuous and variable environments over a time band. Evaluation of these damage mechanisms with in vitro, in vivo, and finite element analysis methods will make a great contribution to the service life of the preferred material. For this reason, many researchers have developed various test methods in the literature.[1-5] However, it may not always be possible to model a living structure with all its parameters in a laboratory environment. The scope of this study is to discuss the process of modeling the effective parameters that occur during chewing movements in a laboratory environment. Biomaterials placed in the mouth during chewing movements may be exposed to many effective parameters such as chewing bite force, corrosive ambient, and thermal change. The ability to model these effective parameters in the test method in the region close to the living body will have a great impact on the consistency of the test results. It may not always be possible to simulate the temperature environment in the human oral cavity region, because it has a continuously variable structure, such as hot coffee and ice cream. For this reason, determining the temperature values as an average in laboratory and finite element analysis method studies can make a great contribution to the stability of the results. The main sources of temperature stabilization in the mouth are the cheeks, tongue, and periodontal tissues surrounding the teeth, which act as a physical barrier that regulates the temperature distribution of the samples.[6] Food and liquids that people swallow during chewing can range from 0°C to 100°C, and the temperature range that an individual can tolerate can vary among different populations and can depend on variables such as the number of teeth, amount of dentin, degree of keratinization of the oral mucosa, and the sex of the patient in the human factory.[7] In addition, dental materials placed in the mouth are exposed to continuous and extreme changes in the oral environment due to temperature and pH fluctuations, where temperature cycling simulates the entry of hot and cold substances into the oral cavity and demonstrates the linear thermal expansion coefficient relationship between the tooth and the restorative material.[8] It has been reported in the literature that pH change in the oral environment is an effective parameter on the wear resistance of the composite material.[9] The effect of artificial aging of dental materials during thermal cycling can be twofold. This effect causes an improvement in the mechanical behavior of the material, primarily water absorption in composite materials. In the second stage, it can reduce wear by creating a lubricating effect during the wear mechanism. Therefore, it is inevitable for teeth and dental materials to be exposed to thermal stress when the temperature changes. This thermal stress can cause wear mechanisms to lose more material. In addition, this situation can create some problems in terms of material integrity and esthetics. As a result, researchers must take the thermal change effect into account in laboratory and finite element analysis methods and in vitro chewing test experimental designs.
Ibrahim Kaya, Yavuz Güler, Mustafa Nalbantoğlu
Abstract
This study introduces a proportional-integral-derivative plus second-order derivative (PIDD2) controller, featuring an innovative multiplier to address time delays, utilizing the direct synthesis method (DSM) for load frequency control (LFC) in time-delayed microgrid (MG) systems. The parameters of the proposed PIDD2 controller are tuned using the direct synthesis method, an analytical tuning approach. In the proposed design method, the best values of PIDD2 controller parameters were found by using the integral of time-weighted absolute error (ITAE). The proposed design approach has been developed for four different scenarios of time-delayed MG systems and compared with existing studies in literature. Real system data were utilized to illustrate the relevance of the proposed design methodology in real-time systems characterized by unmodeled dynamics, measurement noise, parameter fluctuations, and stochastic load variations. Based on Monte Carlo simulations conducted under parameter uncertainties, the proposed PIDD2 controller demonstrated improvements of up to 55.22 % in the ITAE index, 20.19 % in settling time, and 33.73 % in overshoot. Moreover, it significantly enhanced system stability across different random load pattern segments, with Integral of Time weighted Absolute Error (ITAE) improvements reaching as high as 99.99 %. The findings of this study provide significant insights into enhancing the stability of MG systems and improving efficiency in energy management.
Kaya, I., Güler, Y., & Nalbantoğlu, M. (2025). Direct Synthesis-based optimal PIDD2 controller design for enhanced load frequency control in microgrid systems with fuel cells and diesel generators. International Journal of Hydrogen Energy, 147, 149981.