...Tihomir Varchev; Jürgen Mathes; Christian Koch; Stephan Staudacher A machine learning-based approach is presented, which allows to detect persistent engine faults after a single flight. It utilizes transient in-flight measurements and a transient engine model. The time series of the residuals...

... sufficient data of eroded cases for predictive analysis is challenging. Therefore, this paper proposes a blade erosion prediction method using numerical simulation and machine learning. Pressure data of several blade erosion cases are collected from the numerical turbine simulation. The machine learning...

...-mail:  chris.hill@ansys.com e-mail:  florian.menter@ansys.com e-mail:  saurabh.patwardhan@ansys.com e-mail:  ishan.verma@ansys.com 24 10 2023 06 12 2023 26 02 2024 turbulence modeling machine learning neural networks gas turbine combustion swirling flows...

... to operate. Over the last decade, there have been advances in the development of chemometric models, which use machine learning to correlate infrared spectra of fuels to fuel properties like DCN, density, and C/H ratio, among many others. These techniques have certain advantages over the ASTM methods...

... are described. The approach for autonomous selection of the architectures and hyperparameters of Machine Learning (ML) models is explained. The uncertainty quantification techniques are considered. The developed ML-powered methods for compressor geometry prediction are discussed. The ML models' accuracy values...

... in a specific configuration that enabled sharp images to be acquired at these extreme conditions. The collected images were processed using a purposely developed and trained machine learning (ML) algorithm to detect and characterize the droplets' evaporation regime. The results revealed different evaporation...

.... Traditional methods of testing and optimizing the performances of IC engine are complex, time-consuming, and expensive. This has led the researchers to shift their focus to faster and computationally feasible techniques like soft computing (SC) and machine learning (ML) algorithms, which predict the optimum...

...-driven approach for tackling the computational overhead of injector simulations, whereby the transient injection profiles are emulated for a side-oriented, single-hole diesel injector using a Bayesian machine-learning framework. First, an interpretable Bayesian learning strategy was employed...

... analysis. e-mail:  m.burlaka@softinway.com e-mail:  l.moroz@softinway.com 11 07 2022 11 08 2022 19 10 2022 axial compressor performance map machine learning artificial neural network compressor vanes' angles optimization Stennis Space Center 10.13039/100006205...

...Michael T. Tong With the rise in big data and analytics, machine learning is transforming many industries. It is being increasingly employed to solve a wide range of complex problems, producing autonomous systems that support human decision-making. For the aircraft engine industry, machine learning...