Zoekresultaten

Thermodynamics, environmental damage cost, exergoeconomic, life cycle, and exergoenvironmental analyses of a JP-8 fueled turbodiesel aviation engine at take-off phase

The objective of this study is to evaluate the energetic, exergetic, sustainability, economic and environmental performances of a 4-cylinder turbodiesel aviation engine (TdAE) used on unmanned aerial vehicles for the take-off operation mode to assess the system with large aspects. Energy efficiency of the system is found as 43.158%, while exergy efficiency 40.655%. Thermoeconomic analysis gives information about the costs of the inlet and outlet energy and exergy flows of the engine. Hourly levelized total cost flow of the TdAE is found as 21.036 $/h, when the hourly fuel cost flow of the engine is found as 30.328 $/h. The waste exergy cost parameter is determined as 0.0144 MJ/h/$ from exergy cost-energy-mass (EXCEM) analysis, while it is estimated as 14.043 MJ/$ from modified-EXCEM analysis. Environmental damage cost analysis evaluates the cost formation of the exhaust emissions. The total environmental damage cost of the TdAE is computed as 12.895 $/h whilst specific environmental damage cost is determined as 0.054 $/MJ for 494.145 MJ/h TdAE power production. It is assessed that the main contributors to the environmental impact rate of the TdAE are the fuel consumption and the formation pollutants of combustion reaction.

Tourism and climate change mitigation : methods, greenhouse gas reductions and policies

Towards trustworthy data-driven gas turbine prognostics

Trustworthy data-driven prognostics in gas turbine engines are crucial for safety, cost-efficiency, and sustainability. Accurate predictions depend on data quality, model accuracy, uncertainty estimation, and practical implementation. This work discusses data quality attributes to build trust using anonymized real-world engine data, focusing on traceability, completeness, and representativeness. A significant challenge is handling missing data, which introduces bias and affects training and predictions. The study compares the accuracy of predictions using Exhaust Gas Temperature (EGT) margin, a key health indicator, by keeping missing values, using KNN-imputation, and employing a Generalized Additive Model (GAM). Preliminary results indicate that while KNN-imputation can be useful for identifying general trends, it may not be as effective for specific predictions compared to GAM, which considers the context of missing data. The choice of method depends on the study’s objective: broad trend forecasting or specific event prediction, each requiring different approaches to manage missing data.

Gas sensing performance at room temperature of nanogap interdigitated electrodes for detection of acetone at low concentration

From the article: "A facile approach for the fabrication of large-scale interdigitated nanogap electrodes (nanogap IDEs) with a controllable gap was demonstrated with conventional micro-fabrication technology to develop chemocapacitors for gas sensing applications. In this work, interdigitated nanogap electrodes (nanogap IDEs) with gaps from 50–250 nm have been designed and processed at full wafer-scale. These nanogap IDEs were then coated with poly(4-vinyl phenol) as a sensitive layer to form gas sensors for acetone detection at low concentrations. These acetone sensors showed excellent sensing performance with a dynamic range from 1000 ppm to 10 ppm of acetone at room temperature and the observed results are compared with conventional interdigitated microelectrodes according to our previous work. Sensitivity and reproducibility of devices are discussed in detail. Our approach of fabrication of nanogap IDEs together with a simple coating method to apply the sensing layer opens up possibilities to create various nanogap devices in a cost-effective manner for gas sensing applications"

Explainable artificial intelligence for exhaust gas temperature of turbofan engines

Data-driven modeling is an imperative tool in various industrial applications, including many applications in the sectors of aeronautics and commercial aviation. These models are in charge of providing key insights, such as which parameters are important on a specific measured outcome or which parameter values we should expect to observe given a set of input parameters. At the same time, however, these models rely heavily on assumptions (e.g., stationarity) or are “black box” (e.g., deep neural networks), meaning that they lack interpretability of their internal working and can be viewed only in terms of their inputs and outputs. An interpretable alternative to the black box models and with considerably less assumptions is symbolic regression (SR). SR searches for the optimal model structure while simultaneously optimizing the model’s parameters without relying on an a priori model structure. In this work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at high frequencies through the entire flight, in order to uncover meaningful algebraic relationships between the EGT and other measurable engine parameters. The experimental results exhibit promising model accuracy, as well as explainability returning an absolute difference of 3°C compared to the ground truth and demonstrating consistency from an engineering perspective.

AI-based exhaust gas temperature

Data-driven condition-based maintenance (CBM) and predictive maintenance (PdM) strategies have emerged over recent years and aim at minimizing the aviation maintenance costs and environmental impact by the diagnosis and prognosis of aircraft systems. As the use of data and relevant algorithms is essential to AI-based gas turbine diagnostics, there are different technical, operational, and regulatory challenges that need to be tackled in order for the aeronautical industry to be able to exploit their full potential. In this work, the machine learning (ML) method of the generalised additive model (GAM) is used in order to predict the evolution of an aero engine’s exhaust gas temperature (EGT). Three different continuous synthetic data sets developed by NASA are employed, known as New Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS), with increasing complexity in engine deterioration. The results show that the GAM can be predict the evolution of the EGT with high accuracy when using several input features that resemble the types of physical sensors installed in aero gas turbines currently in operation. As the GAM offers good interpretability, this case study is used to discuss the different data attributes a data set needs to have in order to build trust and move towards certifiable models in the future.