Analysis Of Noise Contributions Research Articles

Atom number fluctuations in Bose gases—statistical analysis of parameter estimation

Abstract The investigation of atom number fluctuations in quantum gases at finite temperatures showcases the ongoing challenges in understanding complex quantum systems. Recently, the microcanonical nature of atom number fluctuations in weakly interacting Bose-Einstein condensates was observed. This motivates an investigation of the thermal component of partially condensed Bose gases, due to the conservation of the total atom number.

Here, we present a combined analysis of both components, including a comprehensive analysis of the uncertainties in the preparation and parameter extraction of partially condensed quantum gases. This enables a complementary observation of the thermal atom number fluctuations and yields and improved value of the peak BEC atom number fluctuations $\Delta N_\mathrm{p,0}^2 =(3.7\pm7)\times10^5$ close to the critical temperature. This corresponds to a reduction by \SI{41}{\percent} with respect to previous analysis and corroborates the microcanonical nature of the fluctuations.

The analysis of noise contributions due to the preparation and evaluation of partially condensed Bose gases is based on Monte Carlo simulations of optical density profiles. Importantly, this allows for an estimation of the technical noise contributions to the atom number and temperature, which is generally applicable in the field of ultracold atoms.

Operational transfer path analysis based on neural network

Noise, vibration, and harshness (NVH) issues induced by vibrating source to receivers are sensitive concerns in the automotive industry, significantly determining the product quality. Transfer path analysis (TPA) serves as a crucial tool in addressing these concerns. However, classical and component-based TPA methods necessitate the measurement of all transfer functions associated with the NVH phenomenon, causing high experimental costs; moreover, operational TPA (OTPA) involves matrix inversion processes that may result in the loss of important information. In this study, a neural network-based OTPA (NOTPA) method is proposed that eliminates the need for a matrix inversion process and utilizes only operational measurement. To derive the physical meaning of transmissibility obtained from the trained neural network model, the learning algorithm for NOTPA was developed, and appropriate learning rules were applied to the model. In the general neural network model, training was performed using real-valued parameters. However, the phase of the signal is crucial information in the theoretical formulation of TPA; thus, a complex-valued propagation algorithm was established. Furthermore, an appropriate activation function was chosen to derive the actual transfer mechanism from the trained complex-valued parameters. To experimentally verify the NOTPA method, a testbed resembling automotive vehicle structure was constructed. Estimation of sound pressure and noise contribution analysis were conducted using the NOTPA method and compared with conventional TPA methods. The proposed method demonstrated higher accuracy compared to the conventional OTPA method and successfully identified the main transfer path compared to the classical TPA. Furthermore, by comparing the estimated sound pressure according to the architecture of the model, the consistency of the proposed model was verified.

Noise contribution analysis of road noise with transfer path analysis based on neural network in the box car system

This paper introduces a new approach for transfer path analysis (TPA) combined with neural network technique. For the TPA experiment, the box car model consisting of active and passive systems were manufactured, and road noise simulation experiment were conducted through shaker excitation at various locations. Time domain data was measured and converted into a large amount of complex-valued frequency domain data according to the acquisition block for training the neural network model. The number of layers and nodes of the model were determined by considering the physical characteristics of the system, and normalization and momentum were adjusted to improve accuracy and calculation efficiency. The reliability of the model was verified by comparing the noise contribution results with the classical TPA method. Unlike the existing TPA methods, only the operating data is needed to perform the neural network based TPA. This proposed method reduces the cost of measurement step such as impact test to identify the propagation paths of the noise.

Managing noise from port operations via the use of automated noise monitoring systems

Ports are key pieces of infrastructure that enable the transportation of goods and passengers. Bays Port, Sydney NSW is located in close proximity to communities and balances the increased demand for Port facilities and services with community expectations, environmental requirements and regulatory approvals. For noise generated from Port activities at Bays Port, historically, individual port users have been required to monitor and evaluate noise under different environmental requirements and planning approvals, which can lead to inconsistent reporting, noise limits and regulation between operators and a lack of clarity for the community. Port Authority of NSW has implemented a first of its kind policy in Australia for the management of Port noise from both individual vessel and landside noise sources. To achieve these objectives, an automated directional noise monitoring system has been implemented to provide real-time measurement and analysis of noise contributions from vessel and landside activities. The system utilizes live data of individual vessel tracking locations, meteorology and an array of 6 direction microphones located around the port infrastructure. This information feeds into a live platform to facilitate management of port noise operations. This paper provides an overview of the Port Noise Policy and the use of unattended directional noise monitoring systems to meet the policy’s aims. Case studies are also provided to illustrate the benefits of real time monitoring systems in pro-actively managing port noise.

Numerical Investigation of Background Noise in a Circulating Water Tunnel

The presence of excessive background noise in hydrodynamic noise experiments conducted in circulating water tunnels can significantly impact the accuracy and reliability of experimental test results. To address this issue, it is crucial to evaluate and optimize the background noise during the design stage. In this research, acoustic field model and fluid–solid coupling numerical calculation model of circulating water tunnels are established. Utilizing the finite element method, we analyze the flow noise and flow-excited noise resulting from wall pressure pulses in the circulating water tunnel. Furthermore, we conduct a noise contribution analysis and explore strategies for structural vibration noise control. The results demonstrate that both flow noise and flow-excited noise decrease with increasing frequency, with flow-excited noise being the primary component of the tunnel’s background noise. The presence of resonant peaks significantly contributes to the elevated flow-excited noise levels. Moreover, enhancing structural stiffness and damping proves less effective in suppressing low-frequency peaks. Additionally, employing sound measurement pods suspended from the side of the test section for noise measurement exhibits a high error rate at low frequencies. This research provides insights into optimizing background noise in water tunnels, thereby informing future enhancements in tunnel design.

Noise contribution analysis of a U-shaped girder bridge with consideration of frequency dependent stiffness of rail fasteners

The U-shaped girder has been widely constructed in urban rail transit systems, and related investigations have been conducted to study the vibration and noise radiation. However, the constant stiffness fastener (CSF) model was used in most previous research when simulating the noise radiation of U-shaped girder bridges. Moreover, the sound radiation from piers was generally ignored in noise prediction. In this study, a numerical model of U-shaped girder bridge with piers and frequency dependent stiffness fasteners (FDSF) was developed. Then, the frequency domain method was used to simulate both the dynamic response of the bridge and the associated noise radiation. Field tests were also conducted to verify the accuracy of the proposed method. Finally, the acoustic contribution and sound pressure distribution of the pier and girder were discussed based on the FDSF model. The research shows the simulated dynamic responses agree well with the experimental results in the frequency range of interest (20 to 200 Hz). The predicted noise by using FDSF model is closer to measured results than the case using constant fastener stiffness, which indicates that the frequency dependence of rail fasteners should be considered in noise simulation. The piers have obvious influence on the vibration and noise radiation of the bridge structure, especially at the support pier section, where the noise level is larger than that at the mid span. The structure-borne noise from the pier is distributed in a narrow frequency band (50 to 80 Hz), and only affects the acoustic field in the limited space around the pier. In contrast, the sound contribution of the girder noise to the total noise level is dominant in most of the area.

Engine Noise Contribution Analysis by Operational TPA for Improvement Examination

Engine Noise Contribution Analysis by Operational TPA for Improvement Examination

OTPA 방법을 이용한 롱레일이 적용된 차량용 시트의 구동소음 기여도 분석 연구

OTPA 방법을 이용한 롱레일이 적용된 차량용 시트의 구동소음 기여도 분석 연구

Power-based application of frequency-averaged 𝓁1-norm regularisation technique for the synthesis of accelerating indoor tyre pass-by noise

Pass-by noise contribution analysis is an engineering procedure employed to estimate the contributions from various noise sources on a vehicle to the overall sound pressure level. This can be realised by placing a set of microphones close to the various sources to estimate their source strengths and then synthesising the response at a far-field linear array in the presence of the remaining sources. The results described in this paper rely on measured near-field pressure data close to the tyres of an electric vehicle under accelerating conditions. The number and position of the estimated virtual source strengths used is a compromise between complexity and accuracy, which has previously been addressed mostly empirically. A power-based, frequency-averaged 𝓁1-norm regularisation technique is investigated to optimise the equivalent source position and strength for one operating tyre and, subsequently, the far-field pass-by noise pressure estimates. It is shown that for the tyre under investigation, optimising the positions of only two equivalent sources over the frequency range of interest gives a good representation of the measured far-field spectra.

On the influence of cyclic variability on surface noise contribution analysis of internal combustion engines

Practical mechanical systems often operate with some degree of uncertainty. The uncertainties can result from poorly known or variable parameters, from uncertain inputs or from rapidly changing forcing that can be best described in a stochastic framework. In automotive applications, cylinder pressure variability is one of the uncertain parameters that engineers have to deal with when designing and analyzing internal combustion engines. The characterization of acoustic radiation patterns of internal combustion engines is a challenging task required for the purpose of effective noise reduction. In this paper the influence of cylinder pressure cyclic variability on the assessment and ranking of the different radiating engine surfaces is investigated. A surface contribution analysis (SCA) within a wave based method (WBM) framework is adopted for the assessment of noise radiated from different vibrating surfaces of an engine structure. The method adopted consists in the decomposition of the boundary conditions of the WBM model, assuming the linearity of the vibrational problem associated to the generation of the vibrations on the structure surface, for which the superposition principle is valid. In order to investigate the cyclic variability of cylinder pressure, a Monte Carlo approach is adopted. Starting from measured cylinder pressures that exhibits cyclic variability, random Gaussian distribution of the equivalent force applied on the piston is generated. The results obtained from this analysis are used to derive correlations between cyclic variability and statistical distribution of the results. The statistical information derived can be used to advance the knowledge of the WBM and SCA applications when uncertain inputs are considered.

Noise contribution analysis of a vehicle passenger compartment

In this study, integration of acoustic source quantification (ASQ) and SEA methodologies is aimed to model the interior noise level inside the passenger compartment. The ASQ direct method, briefly explained in theory section, has been used to perform panel acoustic contribution analysis (PACA), in order to investigate regions with panels that contribute primarily to the interior noise level and to determine opportunities for refining the interior noise levels within vehicles. To determine the airborne contributions of engines, the ASQ indirect method, briefly explained in theory section, has been applied. By creating a contribution model using MATLAB (PACA and engine ASQ), the interior noise level of the vehicle has been calculated and correlated with the test results. By performing further analysis on the interior noise contribution model and focusing on the panel contributions, the regions with the most contributing panels have been determined and interior noise refining actions have been applied. By applying interventions, which are the refining outputs of the panel acoustic contribution analysis, an improvement in the A-weighted level of up to 3 dB for a few engine speed ranges has been obtained for the overall noise level of the vehicle. In addition to the panel contribution analysis, which is the primary focus of this study, thickness optimization of the inner dash isolator has been performed for a correlated SEA model, which confirms that no performance loss occurred in the acoustic transfer functions from the engine to the passenger compartment. The physical prototype of the optimized inner dash isolator has been assembled in the vehicle during the panel contribution analysis validation tests.

Further Development of the PNCA: New Panel Noise Contribution Reference-Related (PNCAR)

The Panel Noise Contribution Analysis (PNCA) is a well-known methodology for an airborne Transfer Path Analysis (TPA) in car interior. Pressure contribution from the individual panels at a reference point can be very accurately calculated. Acoustic Trim package treatment can therefore be optimized in terms of frequency and panel area which saves money and time. The method uses only one type of sensors so called particle velocity probes for measuring source strength as well as transfer function (with a reciprocal measurement). Traditionally the PNCA makes use of a big amount of probes at fixed points (about 50) hence non-stationary conditions can be measured as well. Typically the measurement is performed in 3 sessions resulting in 150 individual panels. Because of the low spatial resolution the method can only be used at mid-low frequency range. The new Panel Noise Contribution Analysis Referenced (PNCAR) implements a new post-processing technique which uses a reference sensor in order to extract the relative phase information for the individual panel and frequency of interest. This technique makes it possible to use a limited amount of probes for measuring a whole car interior. Typically 11 probes are used in 11 sessions. Consequently, the methodology becomes more robust and commercially more affordable than conventional PNCA. A real measurement in driving condition was done on the highway in order to validate the methodology. Two different approaches to apply the theory derivations are compared and discussed.