... powertrains for different drive cycles, the current piece of work seeks to combine the advantages of machine learning techniques and physical meanline modelling to facilitate faster, more accurate predictions of complete turbocharger maps. This paper presents a novel methodology for turbocharger turbine...

... margins. The availability of such information enables the opportunity to make technical-economical decisions about the reasonability of implementation of independent variable vanes and their number during engine system analysis. axial compressor performance map machine learning artificial neural...

... diagnostics multiple failures gas turbine health monitoring failure classification gas turbine diagnostics machine learning artificial intelligence Proceedings of ASME Turbo Expo 2022 Turbomachinery Technical Conference and Exposition GT2022 June 13-17, 2022, Rotterdam, The Netherlands GT2022-82037...

... Abstract This study outlines a machine learning approach for long-term stress-rupture (SR) prediction of high temperature austenitic stainless steel. Traditional methods of lifetime estimation and alloy design for turbomachinery application rely mostly on repeated testing, prior experience...

... will likely only be accomplished through the use of machine learning. This study aims to develop and deploy a real-time monitoring technique which integrates flame image classification by a convolutional neural network (CNN) and ionization current signal analysis with the goal of determining detonation wave...

... examples and demonstrate that the discovered formulas can predict the future damage accurately. Our framework is flexible and easily applicable to all areas of science and engineering. With cutting-edge machine learning tools, researchers can simply input the experimental data and then the physics formulas...

... the identified range. The simulation results were used as training and test data to create a model by machine learning methods. Different machine learning methods such as neural network, random forest tree and k-nearest neighbor were applied and compared to determine the best fitted model. Based...

... Abstract The present paper presents an enhanced method for multi-disciplinary design and optimization of centrifugal compressors based on Machine Learning (ML) algorithms. The typical approach involves the preliminary design, the geometry parameterization, the generation of aero-mechanical...

... Abstract OEMs, service providers and end-users are moving from preventative to predictive maintenance to minimize the risk of unwanted power plant shut-downs and to maximize profitability. Digital Twin and Machine Learning (ML) are important techniques in this transformation as it complements...

... industrial scenarios through the above research. multi-parameter prediction deep learning machine learning recurrent neural network convolutional neural network steam turbine Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference and Exposition GT2021 June 7-11, 2021, Virtual...

.... This is exemplified in the process of designing the individual components of the IGT resulting in a potential unrealized efficiency. To overcome the aforementioned challenges, we demonstrate a probabilistic inverse design machine learning framework, namely Pro-ML IDeAS, to carry out an explicit inverse design. Pro-ML...

... with NACA 4digit profiles with sinusoidal leading edges to measure losses according to the Lieblein’s approach. The flow field simulated with RANS strategy was investigated using an unsupervised machine learning strategy to classify and isolate the turbulent wake downstream of the cascade...

... of life consumption of critical parts. Each of these pilot scale projects is summarized with key results presented. data fusion combustion turbines gas turbines machine learning power generation reliability Proceedings of ASME Turbo Expo 2021 Turbomachinery Technical Conference...

... Abstract This study proposes machine learning models to predict the performance of a multi-stage ammonia-water radial turbine using variable nozzle operation under different operating conditions. A 1.2 MW four-stage ammonia-water radial turbine is firstly designed. Then, the one-dimensional off...

... develops when the fuel and air mix as they flow inside the tube. This paper presents a study combining machine learning methods and physical analysis that is aimed at predicting autoignition in such flows. A model for the prediction of autoignition of a fuel jet in a flow configuration referred...

.... Successful identification of wave behavior using image classification serves as a stepping stone for further machine learning integration in RDE research and comprehensive real-time diagnostics. rotating detonation engine machine learning modal classification high-speed imaging neural network...

... and control inputs from pilots. More accurate trend monitoring of the relationship among engine parameters enables earlier anomaly detection with high accuracy. Machine learning techniques such as clustering, nonlinear regression, classification and optimization are used in STM. The input data for generating...

... to accurately predict heat transfer in film cooling flows. Recent work suggests the use of machine learning models to improve turbulent closure in these flows. In the present work, a machine learning model for spatially varying turbulent Prandtl number previously described in the literature is applied...

... engines, which have been achieved by application of the latest numerical design tools, advanced statistical machine learning techniques and utilization of additive manufacturing. Results from testing and validation of the modifications at the Siemens Clean Energy Center (CEC) for stand-alone combustion...

... Abstract 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...