Design and performance of duct acoustic treatment

The procedure for designing acoustic treatment panels used to line the walls of aircraft engine ducts and for estimating the resulting suppression of turbofan engine duct noise is discussed. This procedure is intended to be used for estimating noise suppression of existing designs or for designing new acoustic treatment panels and duct configurations to achieve desired suppression levels

Analytical and experimental studies of acoustic performance of segmented liners in a compressor inlet

The performance of axially segmented (phased) acoustic treatment liners in the inlet of a compressor was investigated. Topics discussed include: (1) the validation of a theoretical procedure to predict propagation and suppression characteristics of duct liners; (2) the in-duct measurement of spinning modes; (3) investigation of phased treatment designs; (4) high Mach inlet acoustic tests; and (5) an experimental investigation of inlet turbulence. The analytical prediction for the multi-segmented treatment was found to provide the correct order of magnitude of suppression and was generally within 50% of that determined experimentally. Refinements required to improve the correlation are identified. Suppression due to high subsonic Mach number flow effects was found to become significant above an average throat Mach number of 0.65 to 0.7 and 20 PNdB was achieved with an average throat Mach number in the range of 0.80 to 0.85. For the measured turbulence in the inlet, including the axial and circumferential turbulence intensities and the axial integral length scale, data are presented with and without an inlet screen showing that the screen reduced the turbulence intensities and that the BPF noise was reduced as a consequence

Analysis, design, and test of acoustic treatment in a laboratory inlet duct

A suppression prediction program based on the method of modal analysis for spinning mode propagation in a circular duct was used in the analytical design of optimized, multielement, Kevlar bulk-absorber treatment configurations for an inlet duct. The NASA-Langley ANRL anechoic chamber using the spinning mode synthesizer as a sound source was used to obtain in-duct spinning mode measurements, radial mode measurements, and far-field traverses, as well as aerodynamic measurements. The measured suppression values were compared to predicted values, using the in-duct, forward-traveling, radial-mode content as the source for the prediction. The performance of the treatment panels was evaluated from the predicted and measured data. Although experimental difficulties were encountered at the design condition, sufficient information was obtained to confirm the expectation that it is the panel impedance components which are critical to suppression at a single frequency, not the particular construction materials. The agreement obtained between measurement and prediction indicates that the analytical program can be used as an accurate, reliable, and useful design tool

Turbofan aft duct suppressor study

Suppressions due to acoustic treatment in the annular exhaust duct of a model fan were theoretically predicted and compared with measured suppressions. The predictions are based on the modal analysis of sound propagation in a straight annular flow duct with segmented treatment. Modal distributions of the fan noise source (fan-stator interaction only) were measured using in-duct modal probes. The flow profiles were also measured in the vicinity of the modal probes. The acoustic impedance of the single degree of freedom treatment was measured in the presence of grazing flow. The measured values of mode distribution of the fan noise source, the flow velocity profile and the acoustic impedance of the treatment in the duct were used as input to the prediction program. The predicted suppressions, under the assumption of uniform flow in the duct, compared well with the suppressions measured in the duct for all test conditions. The interaction modes generated by the rotor-stator interaction spanned a cut-off ratio range from nearly 1 to 7

Turbofan aft duct suppressor study. Contractor's data report of mode probe signal data

Acoustic modal distributions were measured in a fan test model having an annular exhaust duct for comparison with theoretically predicted acoustic suppression values. This report contains the amplitude and phase data of the acoustic signals sensed by the transducers of the two mode probes employed in the measurement. Each mode probe consisted of an array of 12 transducers sensing the acoustic field at three axial positions and four radial positions

Optimization of suppression for two-element treatment liners for turbomachinery exhaust ducts

Sound wave propagation in a soft-walled rectangular duct with steady uniform flow was investigated at exhaust conditions, incorporating the solution equations for sound wave propagation in a rectangular duct with multiple longitudinal wall treatment segments. Modal analysis was employed to find the solution equations and to study the effectiveness of a uniform and of a two-sectional liner in attenuating sound power in a treated rectangular duct without flow (M = 0) and with uniform flow of Mach 0.3. Two-segment liners were shown to increase the attenuation of sound as compared to a uniform liner. The predicted sound attenuation was compared with measured laboratory results for an optimized two-segment suppressor. Good correlation was obtained between the measured and predicted suppressions when practical variations in the modal content and impedance were taken into account. Two parametric studies were also completed

Grammatical evolution decision trees for detecting gene-gene interactions

<p>Abstract</p> <p>Background</p> <p>A fundamental goal of human genetics is the discovery of polymorphisms that predict common, complex diseases. It is hypothesized that complex diseases are due to a myriad of factors including environmental exposures and complex genetic risk models, including gene-gene interactions. Such epistatic models present an important analytical challenge, requiring that methods perform not only statistical modeling, but also variable selection to generate testable genetic model hypotheses. This challenge is amplified by recent advances in genotyping technology, as the number of potential predictor variables is rapidly increasing.</p> <p>Methods</p> <p>Decision trees are a highly successful, easily interpretable data-mining method that are typically optimized with a hierarchical model building approach, which limits their potential to identify interacting effects. To overcome this limitation, we utilize evolutionary computation, specifically grammatical evolution, to build decision trees to detect and model gene-gene interactions. In the current study, we introduce the Grammatical Evolution Decision Trees (GEDT) method and software and evaluate this approach on simulated data representing gene-gene interaction models of a range of effect sizes. We compare the performance of the method to a traditional decision tree algorithm and a random search approach and demonstrate the improved performance of the method to detect purely epistatic interactions.</p> <p>Results</p> <p>The results of our simulations demonstrate that GEDT has high power to detect even very moderate genetic risk models. GEDT has high power to detect interactions with and without main effects.</p> <p>Conclusions</p> <p>GEDT, while still in its initial stages of development, is a promising new approach for identifying gene-gene interactions in genetic association studies.</p

Neural networks for genetic epidemiology: past, present, and future

During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN) are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes