Hybrid Data-Driven and Physics-Based Modeling for Gas Turbine Prescriptive Analytics

This paper presents a methodology for predictive and prescriptive analytics of a gas turbine. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnosis of its flame tube. The developed approach allows not just to analyze and predict some problems in the combustion chamber, but also to identify a particular flame tube to be repaired or replaced and plan maintenance actions in advance.

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Hybrid Data-Driven and Physics-Based Modelling for Gas-Turbine Prescriptive Analytics

10.20944/preprints202009.0460.v1 ◽

2020 ◽

Author(s):

Sergei Belov ◽

Sergei Nikolaev ◽

Ighor Uzhinsky

Keyword(s):

Data Driven ◽

Machine Learning Techniques ◽

Engineering Systems ◽

Learning Techniques ◽

Prescriptive Analytics ◽

Hybrid Data ◽

Complex Engineering ◽

Data Driven Modeling ◽

Complex Engineering Systems

This paper presents a methodology for predictive and prescriptive analytics of complex engineering systems. The methodology is based on a combination of physics-based and data-driven modeling using machine learning techniques. Combining these approaches results in a set of reliable, fast, and continuously updating models for prescriptive analytics. The methodology is demonstrated with a case study of a jet-engine power plant preventive maintenance and diagnostics of its flame tube.

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On the Data-Driven Modeling of Reactive Extrusion

Fluids ◽

10.3390/fluids5020094 ◽

2020 ◽

Vol 5 (2) ◽

pp. 94 ◽

Cited By ~ 2

Author(s):

Ruben Ibañez ◽

Fanny Casteran ◽

Clara Argerich ◽

Chady Ghnatios ◽

Nicolas Hascoet ◽

...

Keyword(s):

Reactive Extrusion ◽

Data Driven ◽

Machine Learning Techniques ◽

Reactive Polymer ◽

Complex Processes ◽

Use Of Data ◽

Learning Techniques ◽

Twin Screw ◽

Data Driven Modeling

This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.

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