A Method to Account for Engine Noise Component Source Locations

Abstract. Noise studies of planar thin-film Ni-P resistors made in/on Printed Circuit Boards, both covered with two different types of cladding or uncladded have been described. The resistors have been made of the resistive-conductive-material (Ohmega-Ply©) of 100 Ώ/sq. Noise of the selected pairs of samples has been measured in the DC resistance bridge with a transformer as the first stage in a signal path. 1/f noise caused by resistance fluctuations has been found to be the main noise component. Parameters describing noise properties of the resistors have been calculated and then compared with the parameters of other previously studied thin- and thick-film resistive materials.

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Applications of finite element and wave envelope element approximations to turbofan engine noise radiation including flight effects

10.2514/6.1984-2333 ◽

1984 ◽

Author(s):

A. PARRETT ◽

W. EVERSMAN

Keyword(s):

Finite Element ◽

Engine Noise ◽

Turbofan Engine ◽

Noise Radiation

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Intervention Modeling

10.1093/oso/9780190661557.003.0005 ◽

2017 ◽

Author(s):

Richard McCleary ◽

David McDowall ◽

Bradley J. Bartos

Keyword(s):

Time Series ◽

Autocorrelation Function ◽

Moving Average ◽

Intervention Model ◽

Noise Component ◽

Autoregressive Integrated Moving Average ◽

Noise Function ◽

Modeling Strategy ◽

The Impact ◽

True Structure

The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.

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