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Organizations invest intense resources in their product development processes. This paper aims to create a knowledge environment using trade-off curves during the early stages of the set-based concurrent engineering (SBCE) process of an aircraft jet engine for a reduced noise level at takeoff. Data is collected from a range of products in the same family as the jet engine. Knowledge-based trade-off curves are used as a methodology to create and visualize knowledge from the collected data. Findings showed that this method provides designers with enough confidence to identify a set of design solutions during the SBCE applications.

Currently, accuracy of existing diesel engine fault diagnosis methods under strong noise and generalisation performance between different noise levels are still limited. A novel multi‐scale CNN‐LSTM neural network (MSCNN‐LSTMNet) is proposed with a residual‐CNN denoising module for anti‐noise diesel engine misfire diagnosis. First, a residual‐CNN module is designed for denoising the original vibration signal measured from the diesel engine cylinder and residual loss for constructing a new loss function is utilised. Considering the essential characteristics of measured vibration signals at different scales, a multi‐scale convolutional NN (CNN) block is designed to realize multi‐scale feature extraction. Specifically, multiple convolution layers with different branches and different convolution kernel sizes are utilised to extract different time scales features, enhancing the robustness of the model. On this basis, the LSTM is utilised to further extract sequential features for improving anti‐noise and generalisation performances. The effectiveness of MSCNN‐LSTMNet is validated by experimental results of both one‐ and hybrid‐cylinder diesel engine misfires diagnosis under various noise levels and working conditions. The results demonstrate that MSCNN‐LSTMNet achieved much better anti‐noise and generalisation performances than the existing methods. Under strong noise conditions (−10 dB signal‐to‐noise ratio) for four datasets, MSCNN‐LSTMNet obtained 97.561% average accuracy, while average accuracy for random forest, deep neural network, CNN and MSCNNNet were 73.828%, 79.544%, 82.247%, and 89.741%, respectively. Moreover, for 11 noise generalisation tasks between different noise levels, MSCNN‐LSTMNet obtained at least 96.679%, 97.849%, 98.892%, and 94.010% accuracy on the four datasets, which are much higher than those of the existing methods.

This study investigates the impact of traffic noise pollution on Elementary School 51 Banda Aceh, which is situated near a major arterial road and a signalized intersection. The primary objectives are to identify key noise sources, evaluate their influence on the school’s learning environment, and assess the overall acoustic comfort experienced by teachers and students. Data were gathered through sound level measurements at various strategic locations around the school, traffic volume monitoring using CCTV, and questionnaires distributed to teachers to understand their perceptions of noise and its impact on classroom activities. The results indicate that noise levels frequently exceed permissible thresholds, reaching up to 76.1 dB. The primary contributors to noise were vehicular engines, horns, and general road activity, which negatively affected teaching effectiveness and student concentration. Teachers reported decreased concentration and increased fatigue, although many have adapted to these conditions. To mitigate the adverse effects of noise pollution, the study recommends implementing natural barriers, such as vegetation, and artificial soundproofing techniques. The findings underscore the necessity for strategic urban and school infrastructure planning to create more conducive learning environments. Further research is recommended to assess the long-term effects of noise exposure on academic performance and mental health.

For modern advanced turbofan engines, the increasing broadband noise of the fan/compressor has become an urgent problem. Based on the 3D fan sound source model, the numerical simulation of the effect of geometric parameters on broadband noise prediction is studied. As a result, (1) the three parameters change the sound power level by affecting the kernel function or the upwash velocity spectrum, and the influence laws are different. (2) If the kernel function is affected, the shape and size of the sound power level spectrum will change; if the upwash velocity spectrum is affected, only the sound power level is changed and the spectrum shape is basically unchanged. (3) The number of rotors only affects the upwash velocity spectrum; the number of stators and the chord length of stators only affect the kernel function.

Indirect noise is a significant contributor to aircraft engine noise, which needs to be minimized in the design of aircraft engines. Indirect noise is caused by the acceleration of flow inhomogeneities through a nozzle. High-fidelity simulations showed that some flow inhomogeneities can be chemically reacting when they leave the combustor and enter the nozzle (Giusti et al., Trans. ASME J. Engng Gas Turbines Power, vol. 141, issue 1, 2019). The state-of-the-art models, however, are limited to chemically non-reacting (frozen) flows. In this work, first, we propose a low-order model to predict indirect noise in nozzle flows with reacting inhomogeneities. Second, we identify the physical sources of sound, which generate indirect noise via two physical mechanisms: (i) chemical reaction generates compositional perturbations, thereby adding to compositional noise; and (ii) exothermic reaction generates entropy perturbations. Third, we numerically compute the nozzle transfer functions for different frequency ranges (Helmholtz numbers) and reaction rates (Damköhler numbers) in subsonic flows with hydrogen and methane inhomogeneities. Fourth, we extend the model to supersonic flows. We find that hydrogen inhomogeneities have a larger impact on indirect noise than methane inhomogeneities. Both the Damköhler number and the Helmholtz number markedly influence the phase and magnitude of the transmitted and reflected waves, which affect sound generation and thermoacoustic stability. This work provides a physics-based low-order model which can open new opportunities for predicting noise emissions and instabilities in aeronautical gas turbines with multi-physics flows.

As battery electric vehicle (BEV) market share grows so must our understanding of the noise, vibration, and harshness (NVH) phenomenon found inside the BEVs which makes this technological revolution possible. Similar to the conventional vehicle having encountered numerous NVH issues until today, BEV has to face many new and tough NVH issues. For example, conventional vehicles are powered by the internal combustion engine (ICE) which is the dominant noise source. The noises from other sources were generally masked by the combustion engine, thus the research focus was on the reduction of combustion engine while less attention was paid to noises from other sources. A BEV does not have ICE, automatic transmission, transfer case, fuel tank, air intake, or exhaust systems. In their place, there is more than enough space to accommodate the electric drive unit and battery pack. BEV is quieter without a combustion engine, however, the research on vehicle NVH is even more significant since the elimination of the combustion engine would expose many noise behaviors of BEV that were previously ignored but would now seem clearly audible and annoying. Researches have recently been conducted on the NVH of BEV mainly emphasis on the reduction of noise induced by powertrain, tire, wind and ancillary system and the improvement of sound quality. This review paper will focus on recent progress in BEV NVH research to advance the BEV systems in the future. It is a review for theoretical, computational, and experimental work conducted by both academia and industry in the past few years.

Failures in a diesel engine will cause massive damage to the machine, so it is essential to monitor and detect unexpected faults in the diesel engine. Because the diesel engine vibration signal is a nonlinear superposition of multiple vibration sources, traditional vibration identification can no longer meet the growing requirements for monitoring accuracy. For this reason, a new method is proposed in this study based on the EMD - nonlinear ICA to separate the noise and other interference signal sources from the engine vibration signal and obtain the fault-related vibration sources; then, a deep learning model is built to identify the fault types. This test assessed the proposed method for diesel engine fault detection. The results signify that the signal separation method can select the fault vibration source from the engine vibration signal and correctly identify the engine faults.

Combustion noise comprises two components: direct combustion noise and indirect combustion noise. The latter is the lesser studied, with entropy noise believed to be its main component. Entropy noise is generated via a sequence involving diverse flow physics. It has enjoyed a resurgence of interest over recent years, because of its increasing importance to aero-engine exhaust noise and a recognition that it can affect gas turbine combustion instabilities. Entropy noise occurs when unsteady heat release rate generates temperature fluctuations (entropy waves), and these subsequently undergo acceleration. Five stages of flow physics have been identified as being important, these being (a) generation of entropy waves by unsteady heat release rate; (b) advection of entropy waves through the combustor; (c) acceleration of entropy waves through either a nozzle or blade row, to generate entropy noise; (d) passage of entropy noise through a succession of turbine blade rows to appear at the turbine exit; and (e) reflection of entropy noise back into the combustor, where it may further perturb the flame, influencing the combustor thermoacoustics. This article reviews the underlying theory, recent progress and outstanding challenges pertaining to each of these stages.

This study examines the management of noise, vibration, and harshness (NVH) in electric vehicle (EV) powertrains, considering the challenges of the automotive industry’s transition to electric drivetrains. The growing popularity of electric vehicles brings new NVH challenges as the lack of internal combustion engine noise makes drivetrain noise more prominent. The key to managing NVH in electric vehicle powertrains is understanding the noise from electric motors, inverters, and gear systems. Noise from electric motors, mainly resulting from electromagnetic forces and high-frequency noise generated by inverters, significantly impacts overall NVH performance. This article details sources of mechanical noise and vibration, including gear defects in gear systems and shaft imbalances. The methods presented in the publication include simulation and modeling techniques that help identify and solve NVH difficulties. Tools like multi-body dynamics, the finite element method, and multi-domain simulation are crucial for understanding the dynamic behavior of complex systems. With the support of simulations, engineers can predict noise and vibration challenges and develop effective solutions during the design phase. This study emphasizes the importance of a system-level approach in NVH management, where the entire drivetrain is modeled and analyzed together, not just individual components.

Altered animal distributions are a consequence of human expansion and development. Anthropogenic noise can be an important predictor of abundance declines near human infrastructure, yet more information is needed to understand noise impacts at the spatial and temporal scales necessary to alter populations. Energy development and associated anthropogenic noise are globally pervasive, and expanding. For example, 600,000 new natural gas wells have been drilled across central North America in less than 20 years. We experimentally broadcast energy sector noise (recordings of compressor engines) in Southwest Idaho (USA). We placed arrays of speakers creating a ‘phantom natural gas field' in a large‐scale experiment and tested the effects of noise alone on breeding songbird abundance. To examine variation in human‐caused noise, we broadcast two types of compressor noise, one with a slightly higher sound intensity and greater bandwidth than the other. Our phantom natural gas field encompassed approximately 100 km2. We broadcast noise over three continuous months, for each of two seasons, and quantified over 20,000 hr of background sound levels. Brewer's sparrows (Spizella breweri) were affected by our narrowband playback, declining 30%, 50 m from the speaker arrays. During our broadband playback, all species combined and Brewer's sparrows decreased 20% and 33%, respectively, at the scale of our sites (~0.5 km2; up to 400 m from speaker arrays). Synthesis and applications. Our results show the importance of incorporating the acoustic structure of noise when estimating the cost of noise exposure for populations. We suggest an urgent need for noise mitigation, such as quieting compressor stations, in energy extraction fields and other sources in natural areas broadly. Resumen Como consecuencia de la expansión humana, la distribución de la fauna silvestre se ha visto alterada. El ruido antropogénico puede ser un indicador de la disminución de fauna cerca de infraestructuras humanas, sin embargo, se necesita más información para entender las consecuencias del impacto humano a una escala que puede alterar poblaciones. La extracción de algunas energías no renovables y el ruido producido durante su desarrollo se está expandiendo a escala global. Por ejemplo, en menos de veinte años se han perforado 600.000 nuevos pozos de gas natural a lo largo de América del Norte. En este estudio, reprodujimos ruido provocado por la extracción de gas natural (grabaciones de los motores de compresores de gas) en el sudoeste de Idaho, Estados Unidos de América. En un experimento a gran escala, colocamos altavoces para recrear artificialmente el sonido de compresores de gas con el objetivo de analizar los impactos del ruido sobre la abundancia de aves en temporada de cría. Para examinar la variación procedente de sonidos causados por actividades de extracción de gas, utilizamos dos tipos de grabaciones, una con mayor intensidad y ancho de banda que la otra. El campo de compresores de gas artificial abarcó aproximadamente 100 km2. Reprodujimos las grabaciones de manera continua durante tres meses a lo largo de dos temporadas y cuantificamos más de 20.000 horas de sonido. Los gorriones de Brewer (Spizella breweri) se vieron afectados por el sonido de banda estrecha disminuyendo su abundancia 30% a 50 m de los altavoces. Durante el sonido de banda ancha, la combinación de todas las especies analizadas y los gorriones de Brewer disminuyeron su abundancia 20% y 33% respectivamente a la escala de nuestra área de estudio (área de aproximadamente 0,5 km2). Síntesis y aplicaciones. Nuestros resultados demuestran la importancia de tomar en cuenta la estructura acústica de los sonidos de origen antropogénico cuando se consideran las consecuencias de actividades humanas sobre poblaciones de vida silvestre. Sugerimos que hay una necesidad urgente de atenuar la contaminación acústica producida por compresores de gas natural y en general lugares donde el ruido antropogénico afecta áreas naturales. Our results show the importance of incorporating the acoustic structure of noise when estimating the cost of noise exposure for populations. We suggest an urgent need for noise mitigation, such as quieting compressor stations, in energy extraction fields and other sources in natural areas broadly.