General Electric Company

Evin Nathaniel Barber

Xi Yang

Daniel Endecott Osgood

Christopher David Barnhill

Brian David Przeslawski

Brian Patrick Peterson

Glen Harold Kirby

Douglas Gerard Konitzer

Stefan Joseph Cafaro

James Ryan Reepmeyer

Johan Michael Reimann

Gagan Adibhatla

Rahim Panjwani

Frederick John Menditto, Iii

Aaron James Schmitz

Suchot Kongsomboonvech

Richard Anthony Zelinski

Iii Frederick John Menditto

A casting assembly includes a core body extending along an axial direction between a first end and a second end, the core body having a core interior surface and a core exterior surface, with the core exterior surface shaping a part surface of a cast part. The core interior surface at least partially bounds a hollow cavity. The casting assembly also includes a shell body extending along the axial direction and having a first shell surface and a second shell surface, with at least a portion of the second shell surface facing the exterior surface of the core body.

Casting assembly

A casting assembly includes at least a first body and a second body. The first body can be a core and the second body can be a shell. The first body has a first interior surface and a first exterior surface. The second body has a second interior surface spaced from a second exterior surface. A portion of the second interior surface is spaced from and facing the first exterior surface. The first body having a casting hollow, where the casting hollow is at least partially defined by the first interior surface.

A control system for active stability management of a compressor element of a turbine engine is provided. In one example aspect, the control system includes one or more computing devices configured to receive data indicative of an operating characteristic associated with the compressor element. For instance, the data can be received from a high frequency sensor operable to sense pressure at the compressor element. The computing devices are also configured to determine, by a machine-learned model, a stall margin remaining of the compressor element based at least in part on the received data. The machine-learned model is trained to recognize certain characteristics of the received data and associate the characteristics with a stall margin remaining of the compressor element. The computing devices are also configured to cause adjustment of one or more engine systems based at least in part on the determined stall margin remaining.

Application of machine learning to process high-frequency sensor signals of a turbine engine

Methods of forming sintered coatings are provided, along with the resulting coatings on a substrate. The sintered coating may comprise a rare earth compound and a sintering aid, with the rare earth compound has the formula: ABZDMOwhere A is Al, Ga, In, Sc, Y, Ce, Pr, Nd, Pm, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Fe, Cr, Co, Mn, Bi, or a mixture thereof; b is 0 to about 0.5; Z is Hf, Ti, or a mixture thereof; D is Zr, Ce, Ge, Si, or a mixture thereof; d is 0 to about 0.5; and M is Ta, Nb, or a mixture thereof. The coating may be densified at a sintering temperature, such as 1300° C. to 1600° C.

Slurry processing for deposition of rare earth hafnium tantalate based barrier coatings

Growing community of inventors

Cincinnati, OH, United States of America

Evin Nathaniel Barber