An instrumentation assembly configured to measure properties of an engine exhaust stream is disclosed in this paper. The instrumentation assembly may include an outer support ring that extends around a central axis, an inner support ring arranged radially inward of the outer support ring around the central axis, and a plurality of instrumentation rake assemblies. The plurality of instrumentation rake assemblies extends from the outer support ring to the inner support ring across an annular passageway defined between the outer support ring and the inner support ring configured to carry the engine exhaust stream.

An apparatus for diagnosing and controlling the aerodynamic stability of a compressor and method there of are provided. The apparatus includes a measurement device (100), a signal processing device (200) and a control and execution device (300), wherein the measurement device (100) is configured to measure the pressure or velocity fluctuations of air flows in different positions inside a compressor in real time, and to transmit real-time measurement signals obtained from different positions to the signal processing device (200); the signal processing device (200) is configured to determine, according to the real-time measurement signals, a type and spatial distribution of instability precursor in the compressor, and to output corresponding control strategy signals to the control and execution device (300); and the control and execution device (300) executes, according to the received control strategy signals, corresponding control actions to regulate the stability of the compressor (S3).

An apparatus for diagnosing and controlling the aerodynamic stability of a compressor and method there of are provided. The apparatus includes a measurement device (100), a signal processing device (200) and a control and execution device (300), wherein the measurement device (100) is configured to measure the pressure or velocity fluctuations of air flows in different positions inside a compressor in real time, and to transmit real-time measurement signals obtained from different positions to the signal processing device (200); the signal processing device (200) is configured to determine, according to the real-time measurement signals, a type and spatial distribution of instability precursor in the compressor, and to output corresponding control strategy signals to the control and execution device (300); and the control and execution device (300) executes, according to the received control strategy signals, corresponding control actions to regulate the stability of the compressor (S3).

A modular and autonomous assembly for detecting the angular position of the blades of an impeller intended to be mounted on a turbine engine, the assembly comprises at least one electrical power source allowing the operation of the elements of the detection assembly independently of the turbine engine on which it is intended to be carried, at least one first sensor intended to be associated with the first impeller, at least one second sensor intended to be associated with the second impeller, and a main housing including a processing unit and storage means.

A method of determining the location and size of a crack in a blade includes measuring a time of arrival of a tip of the blade at an angular position in a rotation, using the time of arrival to calculate a displacement of the tip of the blade, and using the displacements to calculate a first vibration condition and a second vibration condition for the blade. The method also includes comparing the first vibration condition and the second vibration condition for the blade to a predetermined baseline first vibration condition and a predetermined baseline second vibration condition for the blade to determine a change in the first vibration condition and a change in the second vibration condition, and using the magnitude of the change in the second vibration condition relative to the change in the first vibration condition to determine the likely location of the crack and using the magnitude of the change in the first vibration condition and the change in the second vibration condition to determine the size of the crack.

A method of determining the location and size of a crack in a blade includes measuring a time of arrival of a tip of the blade at an angular position in a rotation, using the time of arrival to calculate a displacement of the tip of the blade, and using the displacements to calculate a first vibration condition and a second vibration condition for the blade. The method also includes comparing the first vibration condition and the second vibration condition for the blade to a predetermined baseline first vibration condition and a predetermined baseline second vibration condition for the blade to determine a change in the first vibration condition and a change in the second vibration condition, and using the magnitude of the change in the second vibration condition relative to the change in the first vibration condition to determine the likely location of the crack and using the magnitude of the change in the first vibration condition and the change in the second vibration condition to determine the size of the crack.

Proposed is a dynamometer for measuring load of a rotary shaft to be tested, the dynamometer comprising: a dynamometer shaft connected to the rotary shaft to be tested; a casing part having a through hole through which the dynamometer shaft passes, and a water supply chamber and a drain chamber therein; a stator part including a first stator and a second stator to be spaced apart from each other; a toroidal chamber partitioning part partitioning a toroidal chamber into a first toroidal chamber and a second toroidal chamber, wherein the toroidal chamber partitioning part comprises a runner and a guide ring, forming a drain slot through which the fluid is discharged from the first toroidal chamber and the second toroidal chamber to the drain chamber; and a flow adjusting part adjusting flow rate of the fluid.

Proposed is a dynamometer for measuring load of a rotary shaft to be tested, the dynamometer comprising: a dynamometer shaft connected to the rotary shaft to be tested; a casing part having a through hole through which the dynamometer shaft passes, and a water supply chamber and a drain chamber therein; a stator part including a first stator and a second stator to be spaced apart from each other; a toroidal chamber partitioning part partitioning a toroidal chamber into a first toroidal chamber and a second toroidal chamber, wherein the toroidal chamber partitioning part comprises a runner and a guide ring, forming a drain slot through which the fluid is discharged from the first toroidal chamber and the second toroidal chamber to the drain chamber; and a flow adjusting part adjusting flow rate of the fluid.

In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.

In an example, a method is described. The method includes causing one or more sensors arranged on an aircraft to acquire, over a window of time, first data associated with a first object that is within an environment of the aircraft, where the one or more sensors include one or more of a light detection and ranging (LIDAR) sensor, a radar sensor, or a camera, causing an array of microphones arranged on the aircraft to acquire, over approximately the same window of time as the first data is acquired, first acoustic data associated with the first object, and training a machine learning model by using the first acoustic data as an input value to the machine learning model and by using an azimuth, a range, an elevation, and a type of the first object identified from the first data as ground truth output labels for the machine learning model.