Detection of 5 GHz photons using Al Josephson junctions at 0.7 K

The University of Rhode Island recently completed an assessment of the potential acoustic and other effects of the wind farms on the ecosystem. A developer has proposed to initially construct eight 3.6‐MW wind turbines on lattice jacket structures 5 km south of Block Island and approximately 100 turbines in a second stage 20 km east of Block Island. Construction on the first stage is tentatively planned for summer of 2011, and pile driving will be the main source of noise. The main source of operational noise will likely be vibration from the turbine conducted through the lattice jacket structure into the water. Two passive aquatic listener (PAL) systems were deployed 5 km of Block Island from October 6 to November 11, 2008. Two more PAL systems were deployed on meteorological buoys, one near the first farm and one near the larger farm, for 12 months in 2009/2010. Using data from the PALs, ambient noise budgets and histograms were computed for this pre‐construction phase. The largest sources of noise were found to be shipping, wind, rain, and biological sources. An assessment of the effects of the offshore wind farms will be presented for both the construction and operational phases.

High lift devices, together with landing gears, are the main sources of airframe noise during the approach‐and‐landing phase of aircraft flight. Typical high lift devices include leading edge slats and trailing edge flaps. Other high‐lift‐related noise‐generating devices include spoilers if deployed during a steep approach operation. All the above aerodynamic devices are retracted during the cruise phase of aircraft operation. A slat, when deployed, forms a cove region between the slat and the central main element of the aircraft wing. Flow separation, flow recirculation, an unsteady shear layer, and slat settings together generate noise of mainly broadband content. For a flap, the outboard flap side edge and vortex system associated with it are the main sources of noise. The intensity of high lift device noise generally follows a power law of flow velocity. The main sources of noise are identified and described in this chapter. Introduction is provided concerning main semi‐empirical and computational fluid dynamics methods. Noise attenuation methods are also described.

Raman spectroscopy is a widely used method to investigate chemical molecules by analyzing their vibrational transitions. It utilizes inelastic scattering of the laser light irradiating the investigated object. The scattered light requires appropriate filtering to reduce dominant laser light and expose much weaker components having shifted wavelengths of a characteristic spectral pattern. These components are measured by dispersing onto a detector. The Raman scattered light is very weak and limits applications of Raman spectroscopy. The technique can be potentially applied to investigate in vivo biological tissues to provide real-time diagnosis of serious diseases. One of limitations of the method is caused by problems of identification of a very weak scattered light component at the presence of inherent noise of the measurement setup and external interferences. Usually, the main source of noise in a system is a detector. Various methods can be proposed to reduce its inherent noise. The Raman spectra measurement systems were presented in detail to identify noise sources. The main sources of the detector inherent noise were presented. Next, the signal processing methods, reducing the additive noise component in Raman spectra, were described. Finally, an influence of additive noise in Raman spectra on selected detection algorithms was considered. The results determine how the additive noise component influences concentration accuracy of tissue constituents, identified by popular regression algorithms.

<div class="section abstract"><div class="htmlview paragraph">In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories. Combining the traditional analysis method above, an evaluation process method focusing on the order contribution ratio is proposed, which is employed to quantitatively analyze the main noise sources and guide the focus of noise reduction. The results indicated that the main order contribution sources of noise are the 21st and 42nd orders from the first-stage gear, and the 48th order from the motor. In addition, it is also relatively prominent that the switching frequency component of the inverter in the signal within low-rotational speed. In the speed range of 1000-9600 rpm, the peak value of vibration energy and its change tendency caused by load can be effectively reflected by high contribution proportion of specific orders. It is concluded that the proposed method is equipped to extracting the key sources of vibration problems under various working conditions.</div></div>

Not only the appeal of the sun, natural, and historical beauties but also architectural features and business advantages of the accommodation facilities emerge as important factors in tourism development. Holiday villages differ from other types of accommodation facilities in terms of their functions and services. It is important to provide tourists acceptable levels of comfort in holiday villages offering various functions. One of these comfort conditions is acoustic comfort, which involves noise control. Noise emitted from various indoor and outdoor facilities is the main component impacting acoustic comfort in holiday villages. In this study, a holiday village in Antalya, Turkey with an open area of 120,000m2 was examined to identify noise exposure conditions of outdoor areas. Pools, restaurants, animation areas, playgrounds, and courts are the main outdoor noise sources in this holiday village. The noise emitted by these sources during daytime (Ld) and evening time (Le) are shown in noise maps. The open areas affected by 65 LAeq noise level extend to an area of 55,500 and 21,000m2 during Ld and Le, respectively. With the noise barriers around the main noise sources, impacted open areas are reduced by 13% in Ld and 12% in Le. The results of this study clearly reveals the importance of resolving the issue of environmental noise in the most efficient and cost-effective way in terms of settlement and planning, especially in areas with dominant noise sources like holiday villages.

Noise Analysis of Truck Crane Drive Axle by Using Continuous Wavelet Transform

Pantographs are important devices on high-speed trains. When a train runs at a high speed, concave and convex parts of the train cause serious airflow disturbances and result in flow separation, eddy shedding, and breakdown. A strong fluctuation pressure field will be caused and transformed into aerodynamic noises. When high-speed trains reach 300 km/h, aerodynamic noises become the main noise source. Aerodynamic noises of pantographs occupy a large proportion in far-field aerodynamic noises of the whole train. Therefore, the problem of aerodynamic noises for pantographs is outstanding among many aerodynamics problems. This paper applies Detached Eddy Simulation (DES) to conducting numerical simulations of flow fields around pantographs of high-speed trains which run in the open air. Time-domain characteristics, frequency-domain characteristics, and unsteady flow fields of aerodynamic noises for pantographs are obtained. The acoustic boundary element method is used to study noise radiation characteristics of pantographs. Results indicate that eddies with different rotation directions and different scales are in regions such as pantograph heads, hinge joints, bottom frames, and insulators, while larger eddies are on pantograph heads and bottom frames. These eddies affect fluctuation pressures of pantographs to form aerodynamic noise sources. Slide plates, pantograph heads, balance rods, insulators, bottom frames, and push rods are the main aerodynamic noise source of pantographs. Radiated energies of pantographs are mainly in mid-frequency and high-frequency bands. In high-frequency bands, the far-field aerodynamic noise of pantographs is mainly contributed by the pantograph head. Single-frequency noises are in the far-field aerodynamic noise of pantographs, where main frequencies are 293 Hz, 586 Hz, 880 Hz, and 1173 Hz. The farther the observed point is from the noise source, the faster the sound pressure attenuation will be. When the distance of two adjacent observed points is increased by double, the attenuation amplitude of sound pressure levels for pantographs is around 6.6 dB.

Auto noise is the main contaminated source of urban noises. Accurate identification of auto main noise sources is the premise of controlling auto noise. Sound intensity measurement is an important means of controlling automobiles noises. The principle of sound intensity measurement was discussed in this paper. The device of sound intensity measurement and its operating characteristics was analyzed. By using a sound intensity measurement system to measure noises of a certain kind of vehicle, the surface radiation noises distribution in the engine was researched in the engine. Through Analysis of zing the noise sources according to the test data result, the research results shows that the exhaust system and intake system are the main noise sources. The position of main noise sources could be determined accurately by the sound intensity measurement method. The frequency characteristics of main noise sources could be analyzed conveniently, and the rule and relevant characteristics about the engine noise was obtained. This method can provide a reasonable basis to correspondingly set down the for noise control strategies. Auto noise pollution is one of the major noise sources in the world. The main noises of automobiles are engine noises. It is necessary to reduce engine noises in order to reduce car noise. To do this, the paper must first identify the position and, frequency of its main sound sources at first, and then take some necessary measures to address the main noise sources. With its compact and complicated structure, the internal combustion engine has intense vibrations and a complex working process. Therefore, it is difficult to determine the distribution of the noise source effectively using general acoustic measurement technology. The sound intensity measurement technique is particularly suitable for noise source identification in internal combustion engines. It can effectively measure the size and distribution of the main noise source frequencies. Compared with general acoustic measurement techniques, it does not require a special acoustic environment. It can be done in general internal combustion engine bench test rooms with high efficiency (Pang, 2006).

The accuracy and repeatability of microgravity measurements for surveying purposes are affected by two main sources of noise; instrument noise from the sensor and electronics, and environmental sources of noise from anthropogenic activity, wind, microseismic activity and other sources of vibrational noise. There is little information in the literature on the quantitative values of these different noise sources and their significance for microgravity measurements. Experiments were conducted to quantify these sources of noise with multiple instruments, and to develop methodologies to reduce these unwanted signals thereby improving the accuracy or speed of microgravity measurements. External environmental sources of noise were found to be concentrated at higher frequencies (> 0.1 Hz), well within the instrument's bandwidth. In contrast, the internal instrumental noise was dominant at frequencies much lower than the reciprocal of the maximum integration time, and was identified as the limiting factor for current instruments. The optimum time for integration was found to be between 120 and 150 s for the instruments tested.In order to reduce the effects of external environmental noise on microgravity measurements, a filtering and despiking technique was created using data from noisy environments next to a main road and outside on a windy day. The technique showed a significant improvement in the repeatability of measurements, with between 40% and 50% lower standard deviations being obtained over numerous different data sets.The filtering technique was then tested in field conditions by using an anomaly of known size, and a comparison made between different filtering methods. Results showed improvements with the proposed method performing better than a conventional, or boxcar, averaging process. The proposed despiking process was generally found to be ineffective, with greater gains obtained when complete measurement records were discarded. Field survey results were worse than static measurement results, possibly due to the actions of moving the Scintrex during the survey which caused instability and elastic relaxation in the sensor, or the liquid tilt sensors, which generated additional low frequency instrument noise. However, the technique will result in significant improvements to accuracy and a reduction of measurement time, both for static measurements, for example at reference sites and observatories, and for field measurements using the next generation of instruments based on new technology, such as atom interferometry, resulting in time and cost savings.

This work deals with the prediction of noise generated by gas turbines, which includes engines being designed. One has in mind the fulfillment of the ever-increasing concerns with environment, in particular noise. Analytical and empirical methods have been focused by researchers and industry, although only empirical prediction methods are utilized in this work, for the calculation of the one-third octave band sound pressure levels associated to the main engine noise sources. The methodology for the calculation of the engine noise has been combined with performance and design computational programs to evaluate the noise emitted by each engine component and, by proper combination, the engine total noise. A newly designed and manufactured 5 kN/1.2 MW turbojet engine serves as the basis for the noise prediction. For the study, the main noise sources are: compressor, combustor, turbine and propelling nozzle. In terms of the overall sound pressure level, OASPL, are compatible with the noise produced by similar engines. The noise predictions are performed at engine design speeds in the range of 100% down to 70% of the design speed (28,150 rpm). The engine has not run yet, but it is expected that measured noise will be available in the near future. However, it is important to emphasize that all prediction models used to evaluate the radiated noise from the engine were validated. The engine operating conditions were calculated using a high fidelity engine simulator developed to provide the data used in this study. The methods to estimate the one-third octave band sound pressure levels are reported in NASA TM-195480, SAE ARP-876D, NASA-ANOPP and ESDU Item 98019. No atmospheric attenuation and ground reflection were considered in this work.

Noise source identification with Beamforming in the pass-by of a car

In this study, noise-source identification of a high-speed train was conducted using a microphone array system. The actual sound pressure level analysis of the noise source was performed using scaling factors between the real sound pressure and the beam-power output based on the assumption that the integrated area of the main beam-power lobe is equal to half that of the actual sound pressure of the noise source. Then, the scaling factors for the 144-channel microphone array were derived from analysis of the array response function, and a verification experiment was conducted using a known noise source, an air horn, located on a high-speed train moving at 240 km/h. After the verification test, noise-source identification of the high-speed train was conducted. Based on the resulting noise map of the high-speed train moving at 390 km/h, the main noise sources were determined to be the inter-coach spacing, wheels, and pantograph. The noise generated by the pantograph was then investigated in more detail. It was concluded that the pan head of the pantograph was the main noise source at a frequency of 1000 Hz.

NVH control in construction machinery is multi-disciplinary, comprehensive and complex. In order to achieve noise and vibration control, it is necessary to identify the main noise sources, vibration sources and the corresponding characteristics. To establish a relationship between the machine vibration with the noise sources of an excavator cab, the ICA (independent component analysis) method is employed to separate the multi-channel noise signals into statistically independent components, then utilize time-frequency analysis and correlation analysis to determine the distinct independent noise sources. By introducing energy calculation factor and the mixing matrix A, the contribution corresponding to each noise source can be obtained, which can be utilized to determine the main noise sources. Then by introducing simulation, the correction of the contribution can be verified. By analysis and simulation validation, the effectiveness of our proposed method is demonstrated. Finally, the main noise source is found. Our proposed method can offer an effective guidance to the practical engineering.

In this study, we have investigated the amplitude and intensity of traffic induced noise at noise sensitive institutions of Chittagong city in Bangladesh. The study has been conducted covering four types of sensitive institutions (for instance: schools, colleges and hospitals) from each of the three deliberately selected wards (administrative areas) out of 41 wards of the city. Maximum and minimum sound levels in all the sensitive institutions in average ranged from 65.84 to 79.69 dB and 59.73 to 69.03 dB respectively. In all the 12 different sensitive institutions, noise has exceeded the safe limit (45 dB) as set for noise sensitive institutions such as schools, colleges, hospital, parks by the Department of Environment (DoE), Bangladesh and World Health Organization (WHO). In all the three wards, sound level reached the maximum between 11am and 1pm, both at entrance, in the premises and inside the rooms. Sound levels showed a significant ( P=0.000) declining trend with increase in the distance from the source of noise. The majority of the respondents (73%) have indicated vehicular noise as the main source of noise in their institutes. Buses (29%), Private Cars (27%), Trucks (21%) and Three Wheeler Auto Rickshaws (14%) have been given as the major sources of traffic noise by the respondents. Adverse impacts of traffic induced noise on people inside the sensitive institutions (students in the classroom, and patients, nurses, doctors in the hospitals) have been divided into three categories physical health problems, mental health problems and other noise related problems. Among noise induced physical health problems – headache (79%), and among mental health problems – impaired concentration (82%) have been pointed out by the maximum number of respondents. Moreover, among all other noise induced problems, interference in communication (81%) has been reported as the highest one. The findings of the present study can be considered as baseline data for the decision makers in formulating policies and guidelines regarding the control of noise pollution in context of noise sensitive institutions.

HIGHLIGHTSWe tested the noise of a grain combining harvester using a spiral acoustic array, aiming to identify its main sources and reduce its noise level.The noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz.When the power of other devices is cut off, engine is the main noise source. While all devices are in normal working condition, the main source of noise is the header device and the intermediate conveying device.Abstract. The grain combine harvester is an important agricultural equipment with multiple functions of harvesting, threshing, separating, cleaning and grain gathering. As an instantaneous physical pollution, noise has become one of the main causes of modern civilization diseases. The noise generated by the operation of harvesters not only causes harm to the workers, but also leads to environmental noise pollution. Here, we tested the noise of a grain combine harvester using a spiral acoustic array, aiming to identify its main source by noise source identification technology based on the sound pressure distribution and reduce its noise level. The test results show that the noise of the harvester is mainly concentrated in the frequency range of 1 to 4 kHz. When the power of other devices is cut off, the engine is the main noise source, while under normal working conditions of all devices, the main source of noise is the header device and the intermediate conveying device on the front side of the harvester, the threshing device on the rear side, the engine and the threshing device on the left side, and the engine and the header device on the right side. Keywords: Acoustic array technology, Grain combining harvester, Noise source identification, Vibration and noise reduction.