Cincinnati, OH, United States of America

Richard Anthony Zelinski


 

Average Co-Inventor Count = 10.0

ph-index = 1


Company Filing History:


Years Active: 2024

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1 patent (USPTO):

Title: The Innovations of Richard Anthony Zelinski

Introduction

Richard Anthony Zelinski is an accomplished inventor based in Cincinnati, Ohio. He has made significant contributions to the field of engineering, particularly in the area of turbine engine technology. His innovative approach combines machine learning with high-frequency sensor signals to enhance the performance and stability of turbine engines.

Latest Patents

Zelinski holds a patent for the "Application of machine learning to process high-frequency sensor signals of a turbine engine." This patent describes a control system designed for active stability management of a compressor element within a turbine engine. The system utilizes computing devices that receive data indicative of the operating characteristics of the compressor element. By employing a machine-learned model, the system determines the stall margin remaining of the compressor element based on the received data. This innovative approach allows for adjustments to be made to engine systems, enhancing overall performance and safety.

Career Highlights

Zelinski's career is marked by his work at General Electric Company, where he has been instrumental in developing advanced technologies for turbine engines. His expertise in machine learning and sensor technology has positioned him as a leader in his field. With one patent to his name, he continues to push the boundaries of engineering innovation.

Collaborations

Zelinski has collaborated with notable colleagues, including James Ryan Reepmeyer and Johan Michael Reimann. These partnerships have fostered a creative environment that encourages the development of cutting-edge technologies in turbine engine systems.

Conclusion

Richard Anthony Zelinski exemplifies the spirit of innovation in engineering through his work on turbine engine technology. His contributions, particularly in the application of machine learning, have the potential to significantly improve the performance and reliability of turbine engines.

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