The transportation of oil and gas through pipelines is an integral aspect of the global energy infrastructure. It is crucial to ensure the safety and integrity of these pipelines, and one way to do so is by utilizing an inspection tool called a smart pig. This paper reviews various smart pigs used in steel oil and gas pipelines and classifies them according to pipeline structure, anomaly-detection capability, working principles, and application areas. The advantages and limitations of each sensor technology that can be used with the smart pig for in-line inspection (ILI) are discussed. In this context, ultrasonic testing (UT), electromagnetic acoustic transducer (EMAT), eddy current (EC), magnetic flux leakage (MFL), and mechanical contact (MC) sensors are investigated. This paper also provides a comprehensive analysis of the development chronology of these sensors in the literature. Additionally, combinations of relevant sensor technologies are compared for their accuracy in sizing anomaly depth, length, and width. In addition to their importance in maintaining the safety and reliability of pipelines, the use of ILI can also have environmental benefits. This study aims to further our understanding of the relationship between ILI and the environment.

In this paper, we propose a sophisticated battery model for vanadium redox flow batteries (VRFBs), which are a promising energy storage technology due to their design flexibility, low manufacturing costs on a large scale, indefinite lifetime, and recyclable electrolytes. Primarily, fluid distribution is analysed using computational fluid dynamics (CFD) considering only half-cells. Based on the analysis results, a novel model is developed in the MATLAB Simulink environment which is capable of identifying both the steady-state and dynamic characteristics of VRFBs. Unlike the majority of published studies, the inherent characteristics of the flow battery, such as shunt current, ion diffusion, and pumping energy consumption, are considered. Furthermore, simplified charge transfer resistance (CTR) is taken into account based on electrochemical impedance spectroscopy (EIS) measurement results. The accuracy of the model was determined by comparing the simulation results generated by the equivalent circuit battery model developed in this study with real datasets. The obtained results indicate that the developed model has an accuracy of 3% under the sample operating conditions selected. This study can also be used to fill the gap left by the absence of the VRFB battery model in commonly used programs for renewable energy systems, such as TRNSYS.

Regression algorithms are statistical techniques used to predict the value of a dependent variable, based on one or more independent variables. These algorithms are commonly used in fields such as economics, finance, and engineering. Temperature prediction is a specific application of regression analysis. In this case, the dependent variable is temperature and the independent variables include factors such as humidity, speed of the wind, direction of the wind, and precipitation. There are many different types of regression algorithms, each with its strengths and weaknesses. The study compares the performance of multiple regression models in predicting the average air temperature, using one month's weather data for the Beşiktaş district of Istanbul. A total of 6 different regression models, including ridge, lasso, linear, polynomial, random forest (RF), and support vector (SV) regressions, were included in the study. Among the regression models trained and tested on two different data sets, the three most successful models in predicting average air temperature were lasso, RF, and polynomial regressions (PRs), respectively.

Vehicle integrated photovoltaics (VIPV) is an emerging technology requiring multidimensional analysis to support its standardization and adoption in electric vehicles (EVs). This study examines the integration of photovoltaic (PV) systems into EVs, focusing on dc–dc converter topologies transferring solar energy to the vehicle’s low-voltage (LV) electrical system across various auxiliary battery voltage levels. Three converter topologies, dual-active bridge (DAB), LLC, and flyback converters, are evaluated in terms of efficiency, cost, and volume. System performance is assessed under defined daily driving scenarios and auxiliary load profiles in two U.S. regions, Maryland and Arizona, which differ significantly in solar irradiance. A case study shows VIPV systems can supply up to 60.5% of annual secondary electrical load demands. The findings highlight that the choice of converter topology has a substantial impact on overall system efficiency and energy savings. Additionally, the study explores the environmental implications of large-scale VIPV adoption in the U.S. and globally, illustrating its potential to reduce the carbon footprint of EVs and advance sustainable transportation.