Development of a Data-driven Damage Model for Large Engine On-board In-cylinder Pressure Transducers
The service life of on-board cylinder pressure sensors in large engines is determined not only by loads at nominal operating conditions but to a large extent by irregular events such as knocking combustion. In addition to high thermal loads, extreme rates of pressure rise have an impact since they excite sensor natural frequencies and can thus lead to strong vibrations and deformation of the sensor.
The target of this thesis is to develop a data-driven damage model for a specific in-cylinder pressure transducer based on measurement data from dedicated durability tests that were carried out on a large high-speed single-cylinder research engine (SCE) at the Large Engines Competence Center (LEC). With such a model, the aim is to obtain a detailed understanding of the impact of specific engine operating conditions on pressure transducer degradation.
Familiarization with the engine test setup and corresponding measurement
technology and measurement parameters
Preprocessing of engine measurement data
Generation of value-added data
Establishment of correlations between different measurement techniques
(engine indication system vs. high-speed oscilloscope measurements)
Detailed investigation of potential sensor damage indicators
Development of a data-driven damage model for a specific in-cylinder pressure transducer
Composition of the master’s thesis
Prerequisites: Programming skills in Python and/or R; experience in data analysis
Earliest possible start date: Immediately
Duration: Approximately 6 months
Ao. Univ.-Prof. Dr. Andreas Wimmer, +43 (316) 873-30101, email@example.com
Dr. Constantin Kiesling, +43 (316) 873-30092, firstname.lastname@example.org
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