In the study conducted for the cooling systems of MALE class unmanned aerial vehicles using internal combustion engines, new type radiators were designed using spring-structure fins. Among the radiators formed with spring structures acting as fins, the radiator developed using springs with a pitch of 2.25 mm was named Radiator-Y1, the radiator developed using springs with a pitch of 4.25 mm was named Radiator-Y2, and the radiator developed using springs with a pitch of 8.25 mm was named Radiator-Y3. This design change is seen as an innovative method that can increase heat transfer on the radiator surface and increase cooling performance by increasing the turbulence effect of the air affecting the radiator. Experimental studies were carried out using single type (Al₂O₃ and ZnO) and hybrid (ZnO-CuO) nanofluids in addition to pure water. Experiments were carried out using different air speeds (8–10–12 m/s) and different coolant flow rates (20–22 L/min) and radiator performance was investigated. The effects of the surface area created by the spring structure and the turbulence effect on heat transfer were evaluated. As a result of the studies, Radiator-Y1 showed the best cooling performance among the radiators developed with spring structures, followed by Radiator-Y2 and Radiator-Y3. It was observed that the nanofluids used had a positive effect on the cooling performance compared with pure water, as did the hybrid nanofluid compared when compared with single type nanofluids.

Machining processes often face challenges such as elevated temperatures and wear, which traditional cutting fluids are insufficient to address. As a result, solutions involving nanoparticle additives are being explored to enhance cooling and lubrication performance. This study investigates the effect of thermal conductivity, an important property influenced by the densities of mono and hybrid nanofluids. To this end, various nanofluids were prepared by incorporating hexagonal boron nitride (hBN), zinc oxide (ZnO), multi-walled carbon nanotubes (MWCNTs), titanium dioxide (TiO₂), and aluminum oxide (Al₂O₃) nanoparticles into sunflower oil as the base fluid. Hybrid nanofluids were created by combining two nanoparticles, including ZnO + MWCNT, hBN + MWCNT, hBN + ZnO, hBN + TiO₂, hBN + Al₂O₃, and TiO₂ + Al₂O₃. A dataset consisting of 180 data points was generated by measuring the thermal conductivity and density of the prepared nanofluids at various temperatures (30–70 °C) in a laboratory setting. Conducting thermal conductivity measurements across different temperature ranges presents significant challenges, requiring considerable time and resources, and often resulting in high costs and potential inaccuracies. To address these issues, a feedforward artificial neural network (FFANN) method was proposed to predict thermal conductivity. Our multilayer FFANN model takes as input the temperature of the experimental environment where the measurement is made, the measured thermal conductivity of the relevant nanoparticle, and the relative density of the nanoparticle. The FFANN model predicts the thermal conductivity value linearly as output. The model demonstrated high predictive accuracy, with a reliability of R = 0.99628 and a coefficient of determination (R²) of 0.9999. The average mean absolute error (MAE) for all hybrid nanofluids was 0.001, and the mean squared error (MSE) was 1.76 × 10⁻⁶. The proposed FFANN model provides a State-of-the-Art approach for predicting thermal conductivity, offering valuable insights into selecting optimal hybrid nanofluids based on thermal conductivity values and nanoparticle density.

This study aims to determine the regional hydrogen production potential of 2535 cities using a machine learning model based on renewable energy potential (photovoltaic and wind energy) and hydrogen production capacity data. The performance of the developed Multilayer Perceptron (MLP) model was evaluated using metrics such as R2 (R-Squared), RMSE (Root Mean Squared Error), and MAE (Mean Absolute Error). The model outputs were interpreted using SHAP (SHapley Additive exPlanations) analysis to indicate the effects of parameters influential in renewable energy production, such as solar radiation and wind speed, on hydrogen production. Furthermore, cities with the highest and lowest potential for green hydrogen production were identified and geographically visualized using the Folium library. The results show that the model can predict the regional green hydrogen production potential with high accuracy and map the most suitable regions for production. This study serves as an important guide for sustainable energy planning and the development of green hydrogen infrastructure towards reducing carbon emissions to zero.

Bu çalışmada, Elazığ’da bulunan bir mobilya fabrikasında yakıt olarak doğal gaza dönüştürülen bir buhar kazanına termodinamiğin ikinci kanunu uygulanarak ekserji analizi yapılmıştır. Dünyadaki teknolojik gelişmelere paralel olarak, enerji tüketim miktarlarındaki artış sınırlı enerji kaynaklarının daha verimli kullanılmasının gerekliliğini ortaya çıkarmıştır. Kullanmakta olduğu enerjinin büyük bir bölümünü ithal etmekte olan ülkemizin genel enerji tüketimi içerisinde ısıtma ve soğutma sistemlerinin payı önemli bir yer kaplamaktadır. Bu sistemlere ekserji analizinin uygulanması sistem veriminin değerlendirilmesinde önemli bir parametredir. Yapılan analiz sonucu buhar kazanı ekserji kaybı 15678,19 kW, ekserji verimi ise %56 olarak hesaplanmıştır.