Optical measurement of heteronuclear cross-relaxation interactions in Tm:YAG

We investigate cross-relaxation interactions between Tm and Al in Tm:YAG using two optical methods: spectral holeburning and stimulated echoes. These interactions lead to a reduction in the hyperfine lifetime at magnetic fields that bring the Tm hyperfine transition into resonance with an Al transition. We develop models for measured echo decay curves and holeburning spectra near a resonance, which are used to show that the Tm-Al interaction has a resonance width of 10~kHz and reduces the hyperfine lifetime to 0.5 ms. The antihole structure is consistent with an interaction dominated by the Al nearest neighbors at 3.0 Angstroms, with some contribution from the next nearest neighbors at 3.6 Angstroms.Comment: 13 pages, 9 figure

Concept for measuring aeroacoustic noise transmission in trains derived from experience gained in aircraft testing

Measuring aeroacoustic noise and its transmission into the passenger compartment of an aircraft provides a way to evaluate the vibrational energy caused by the turbulent boundary layer and other aeroacoustic sources, the transmission path and the sound radiation into the compartment. The measurement results are used to validate numerical models and to identify parameters for a turbulent boundary layer model. This was used in a flight test campaign at the DLR to separate different sources and determine their transmission into an aircraft. The measurement concept and results from the flight test will be presented, whereupon the potential in applying this concept to high-speed train measurements will be discussed

Evaluation of the noise impact of flap-tip fences installed on laminar wings

The aeroacoustics of laminar wings and the noise abatement provided by flap-tip fences are studied experimentally on a scaled model installed in the closed test section of a subsonic wind tunnel. The activity has been developed under an international project funded by the European Union through the H2020 Framework Program. The model reproduces an innovative regional aircraft mounting aft-engines and adopting the Natural Laminar Flow concept. Both take-off and landing settings are tested for several combinations of wind tunnel speeds and angles of attack; these configurations being the most critical from the viewpoint of airframe noise generation. A baseline configuration without flap-tip fences is also tested for a comparative study. Noise sources are identified by measuring pressure fluctuations through a phased microphone array. Data are processed using both Conventional Beamforming and CLEAN-SC algorithms to retrieve the sound source maps and the integrated spectra over the areas of interest. Directivity effects are investigated as well by moving the microphone array in different axial positions corresponding to different aircraft polar directivity angles. The wind tunnel data are eventually extrapolated to full scale and projected to flight condition allowing the analysis of the results in terms of Effective Perceived Noise Level (EPNL). Tests provided an extensive characterization of the acoustic behavior of the analyzed model demonstrating the capability of measurements carried out in a non-anechoic environment, to provide reliable data. The longitudinal traversing of the microphones array allowed us to compute, even though qualitatively, the EPNL and thus demonstrated the feasibility of a procedure that can be a reference for the design of future aeroacoustic tests. The effectiveness of the flap-tip fences to successfully reduce the flap side-edge noise has been definitely verified both through the analysis of the acoustic maps retrieved from the beamforming investigation and by the estimation of the overall variation of the EPNL with respect to a baseline reference configuration without low-noise devices

Expert Decision Support System for Aeroacoustic Classification from Deconvolved Beamforming Maps

We present an expert decision support system for time-invariant aeroacoustic source classification from deconvolved beamforming maps and results based on scaled airframe half-model wind tunnel measurements. The system consists of three steps: the identification of acoustic sources from the deconvolved maps, the calculation of their acoustic properties, and the clustering of the sources based on their properties. In this paper, we present and compare two methods to identify and extract source spectra from the beamforming maps. The first relies on the spatial normal distribution of CLEAN-SC results for aeroacoustic broadband sources. The second uses hierarchical clustering methods. We propose a variety of aeroacoustic features that capture the characteristics of the spectra while being independent of absolute parameters such as the Mach number. Based on these features the aeroacoustic sources are clustered using unsupervised machine learning methods to determine similar or atypical behavior. This expert support system helps aeroacoustic specialists in classifying the identified sources to support them in analyzing their typical behavior and identifying spurious sources