Engine Noise Research Articles

Tunable active noise control circuit topology for multiple-feature applications

A tunable active-noise-control (ANC) circuit topology for headsets used in different applications is presented in this article. In the current consumer headset market, ANC is a mature technology that is commonly applied to wireless headsets connected to smartphones for listening to music and making phone calls. The development of ANC headsets has resulted in low-cost and simple devices due to the built-in ANC registers in the digital circuit. Digital circuit implementation is important to provide ultra-low latency processing for different algorithms compared with using digital signal processors (DSPs). However, a limitation associated with these built-in ANC filters is that the ANC digital circuit has been designed only for the wireless headset consumer market. Consequently, ANC headsets can only be used for designated features. ANC tuned for planes by eliminating low-frequency engine noise has been commonly used in the past twenty years. Pass-through (PT) amplification is also commonly tuned to allow users to hear external sounds and have conversations without having to remove their headset. In addition, ANC and PT amplification are also not allowed to operate simultaneously on the existing chipsets. In this paper, an ANC circuit topology with ultra-low latency processing is presented, where the ANC response of the headset can be tuned without any restriction imposed by the built-in ANC filters. The proposed ANC circuit topology consists of a commercial ANC chipset with an external audio codec to provide tunable characteristics. It is achieved by fixed maximum noise cancellation of ANC chipset being modified by summing with the signal from audio-codec path via an audio mixer. Acoustic performance for several headset applications implemented using the proposed circuit is validated. This simple circuit topology can be easily developed and adapted to the current headset consumer market for consumer-selective ANC in different environments.

Optimizing the Shape of Space-Constrained Silencers Composed of Multiple Straight/Reverse Chambers Using the Simulated Annealing Method

Abstract To address the issue of hearing health problems caused by the broadband noise emitted from space-constrained marine diesel engines on a ship, an efficient silencer equipped at the engine's venting outlet is necessary. In consideration of the space-constrained situation that often occurs in ships, a reverse silencer with multiple chambers is introduced. A mathematical model of eigenfunction is adopted to consider the acoustic effect in high frequencies. An optimization of the silencer will be performed using the acoustic model of an eigenfunction together with the Simulated Annealing (SA) method under space-constrained conditions to achieve the best design. To pursue an efficient silencer design, two types of reverse silencers (Silencers A and B) are adopted to reduce the engine's venting noise. Acoustical models for straight chamber and reverse chamber silencers are checked before optimization. To assure the reliability of the SA scheme, a pure tone optimization at 250 Hz is exemplified. Results in silencers A and B show that the Sound Transmission Loss is approximately maximized at the desired frequency. Thereafter, a broadband noise elimination of engine noise using silencers A and B is performed. Simulated results show that overall noise abatement can be improved when the number of inverse chambers increases. The optimization of space-constrained reverse silencers with multiple chambers using eigenfunction in cooperation with Simulated Annealing (SA) has been accomplished. The overall silencer system's four-pole matrix is used to predict the silencer's acoustical performance of STL, which is achieved by multiplying individual four-pole matrices. With the introduction of the reverse silencer, the hearing health of workers can be protected, and noise pollution can be reduced in industrial settings.

Transmission Loss Characteristics of Dual Cavity Impedance Composite Mufflers for Non-Planar Wave Cavity Resonance

In conventional gasoline automobiles, the engine powers the air conditioning system and engine noise can somewhat mask the noise and vibration of the air conditioning system. In pure electric vehicles, however, the absence of an engine makes the air conditioning system’s noise more noticeable, concentrated in a limited frequency range at constant speeds. As a result, aerodynamic noise from the air conditioning system is a primary noise source in electric vehicles. Pipeline silencers are the main method for reducing this noise. The current silencer design uses plane wave acoustic theory but when cavity modal resonance occurs, the transmission loss error is relatively high. This article addresses the issue of non-planar wave cavity resonance, studying the cavity modal of a muffler using the finite element method to reveal the transmission loss under cavity mode resonance. A dual cavity expansion structure of an impedance composite muffler is proposed, with sound-absorbing materials placed in the cavity to enhance acoustic performance. The analysis of the transmission loss characteristics of the impedance composite muffler provides a theoretical basis for noise control in pure electric vehicle air conditioning systems.

Analisis Dampak Kebingaran Mesin Pesawat Bagi Petugas Marshaller di Bandar Udara Internasional Blimbingsari Banyuwangi

The number of aircraft in Indonesia drives both the national and global aviation industries. The government and airport management must enhance the quality, performance, safety, security, and economic development of airports. Blimbingsari International Airport in Banyuwangi supports air transportation in Rogojampi, Banyuwangi; however, advancements in aviation facilities can increase air pollution and noise, disrupting community activities and degrading environmental quality. Aircraft movements require coordination, including the role of marshallers who must use Personal Protective Equipment (PPE) and adhere to Occupational Health and Safety (OHS) protocols for safety. Noise reduction efforts at the airport must comply with the maximum limit of 55 dBA in residential areas. This qualitative case study investigates the impact of aircraft engine noise on the hearing of marshallers at Blimbingsari International Airport in Banyuwangi. Primary data were obtained from direct observation and interviews, while secondary data were sourced from regulations and relevant documents. The findings indicate that noise levels at the airport range from 34-66 dB(A) and do not significantly impact the health of nearby residents or operational staff. The authors recommend that the airport management regularly check the readiness of field staff, enhance equipment and the availability of first aid kits (P3K) in the AMC Unit, and ensure the quality of PPE and health guarantees for operational staff, particularly in the AMC division (Marshallers, Ramp Handling).

A Rotating Machinery Fault Diagnosis Method Based on Dynamic Graph Convolution Network and Hard Threshold Denoising.

With the development of precision sensing instruments and data storage devices, the fusion of multi-sensor data in gearbox fault diagnosis has attracted much attention. However, existing methods have difficulty in capturing the local temporal dependencies of multi-sensor monitoring information, and the inescapable noise severely decreases the accuracy of multi-sensor information fusion diagnosis. To address these issues, this paper proposes a fault diagnosis method based on dynamic graph convolutional neural networks and hard threshold denoising. Firstly, considering that the relationships between monitoring data from different sensors change over time, a dynamic graph structure is adopted to model the temporal dependencies of multi-sensor data, and, further, a graph convolutional neural network is constructed to achieve the interaction and feature extraction of temporal information from multi-sensor data. Secondly, to avoid the influence of noise in practical engineering, a hard threshold denoising strategy is designed, and a learnable hard threshold denoising layer is embedded into the graph neural network. Experimental fault datasets from two typical gearbox fault test benches under environmental noise are used to verify the effectiveness of the proposed method in gearbox fault diagnosis. The experimental results show that the proposed DDGCN method achieves an average diagnostic accuracy of up to 99.7% under different levels of environmental noise, demonstrating good noise resistance.

A Comprehensive Approach to Biodiesel Blend Selection Using GRA-TOPSIS: A Case Study of Waste Cooking Oils in Egypt

The transition to sustainable energy sources is critical for addressing global environmental challenges. In 2017, Egypt produced about 500,000 tons of waste cooking oil from various sources including food industries, restaurants and hotels. Sadly, 90% of households choose to dispose of their used cooking oil by pouring it down the drain or into their village’s sewers instead of using proper disposal methods. The process involves converting waste cooking oil (WCO) into biodiesel.This study introduces a multi-criteria decision-making approach to identify the optimal biodiesel blend from waste cooking oils in Egypt. By leveraging the grey relational analysis (GRA) combined with the technique for order preference by similarity to the ideal solution (TOPSIS), we evaluate eight biodiesel blends (diesel, B5, B10, B20, B30, B50, B75, B100) against various performance metrics, including carbon monoxide, carbon dioxide, nitrogen oxides, hydrocarbons, particulate matter, engine power, fuel consumption, engine noise, and exhaust gas temperature. The experimental analysis used a single-cylinder, constant-speed, direct-injection eight cylinder diesel engine under varying load conditions. Our methodology involved feature engineering and model building to enhance predictive accuracy. The results demonstrated significant improvements in monitoring accuracy, with diesel, B5, and B20 emerging as the top-performing blends. Notably, the B5 blend showed the best overall performance, balancing efficiency and emissions. This study highlights the potential of integrating advanced AI-driven decision-making frameworks into biodiesel blend selection, promoting cleaner energy solutions and optimizing engine performance. Our findings underscore the substantial benefits of waste cooking oils for biodiesel production, contributing to environmental sustainability and energy efficiency.

Evaluation of steering wheel vibration in electric vehicles and application to Dynamic Motor Drive

Eliminating steering wheel vibration is a key in refining vehicle Noise, Vibration, and Harshness (NVH). The absence of masking engine noise and vibration makes this challenge harder in a pure electric vehicle. This paper provides three key insights with regards to the evaluation of steering wheel vibration in EVs using on-road test data. First, we explore the effects of steering wheel grip variation in the measured accelerometer response. Second, we identify the limitations of evaluating powertrain induced vibration in a noisy environment and propose a post-processing technique to reject irregular road-induced perturbations from the measured data. Third, we evaluate a tuned mass absorber as a localized mitigation tool for steering wheel vibration. Finally, we describe how the above techniques were brought together to deliver a refined NVH experience while integrating Dynamic Motor Drive (DMD) technology as a software-only solution which has been shown to improve EV range by 0.4% on IPM and 3% on EESM based vehicles.

An improved limited memory-Sage Husa-cubature Kalman filtering algorithm for the state of charge and state of energy co-estimation of lithium-ion batteries based on hysteresis effect-dual polarization model

The accurate estimation of battery state of charge (SOC) and state of energy (SOE) are the key technologies in researching battery management systems. Due to the uncertainty of prior noise in practical engineering, traditional cubature Kalman filters (CKF) have lower filtering accuracy. This paper proposes an improved limited memory-Sage Husa-cubature Kalman (LM–SH–CKF) algorithm to estimate the SOC and SOE of lithium-ion batteries. During the filtering process, the Sage-Husa estimator is introduced to estimate the noise, correcting the statistical characteristics of the noise in real-time, thereby improving the estimation accuracy. Meanwhile, establish a filtering divergence criterion to determine whether the filtering is abnormal and ensure the real-time performance of the algorithm. To improve the accuracy of parameter identification, the Hysteresis Effect-Dual Polarization (HE-DP) model is established and the limited memory recursive least square (LMRLS) algorithm is proposed. The results show that the LM–SH–CKF has the smallest mean absolute error (MAE) and root mean square error (RMSE). Under the dynamic stress test (DST) at 15 °C, the estimated MAE and RMSE of SOC and SOE are both less than 1 %. The LM–SH–CKF has high accuracy and robustness, and short calculation time, providing a reference for battery status monitoring.

A novel feedforward narrow-broadband hybrid active noise control system for excavator interior noise control

Excavator interior noise is a mixture of engine noise and impulsive noise produced by working device. Hybrid narrow-broadband active noise control (HANC) systems can efficiently eliminate the traditional mixed broad-narrowband noise. However, these HANC systems have drawbacks such as high computational complexity, unable to cope with impulsive noise and inability to obtain the best performance due to parameter selection method. To solve these problems, a novel feedforward narrow broadband HANC system consisting of four subsystems, namely, sinusoidal noise canceller (SNC) subsystem, robust broadband active noise control (RBANC) subsystem, delay narrowband active noise control (DNANC) subsystem, and supporting error calculating subsystem, is proposed. Combining the advantages of delayed least mean square algorithm in DNANC subsystem and Versoria criterion-based filtered-x least mean square algorithm in RBANC subsystem, both the computational efficiency and the robustness when encountering impulsive noise of the proposed HANC system are efficiently improved. In addition, the performance of the proposed HANC system is further improved by changing the key parameters selection method form trial-and-error method to improved particle swarm optimization (IPSO) algorithm with active factor. Both the superiority of the proposed HANC system over existing counterparts and the advantage of the IPSO algorithm with active factor in determining the key parameters are demonstrated by extensive simulations. Real-time HANC experiments in semi-anechoic chamber based on measured excavator interior mixed noise are conducted in this article. Experimental results illustrate that the proposed HANC system achieves good noise elimination performance and also being robust when encountering impulsive noise.

Perancangan Sistem Alarm Kebisingan untuk Kapal Penangkap Ikan Berbasis Arduino Uno dengan Sensor KY-037

Fishermen are highly susceptible to hearing impairment due to prolonged exposure to the high-intensity noise of boat engines. While numerous studies have investigated the noise intensity levels of boat engines, no research has been conducted on noise warning alarm systems for fishing boats. Therefore, this study aims to develop a sound noise alarm system based on Arduino Uno with a KY-037 sensor for fishing boats, which is expected to reduce the potential for hearing impairment among fishermen. This research employs a quantitative descriptive approach, with data collected through experimental methods and analyzed by comparing noise level data at various engine speeds. The results of the noise level measurements of boat engines at the Fish Landing Base (PPI) in Cikidang, Pangadaran Regency, indicate that the majority of fishermen are safe from hearing impairment due to engine noise, as most boats there are small, with a Gross Tonnage (GT) capacity of under 5 GT. The risk of hearing impairment emerges for boats with a capacity of 11 GT running at a maximum engine speed of 1600 rpm, producing noise levels up to 84 dB. Thus, it is recommended that crew members use earplugs.

GTM-120 micro gas turbine engine noise identification

Micro gas turbine engines are a popular subject in research activities. Most of them consider performance measurements, and vibration analysis that are related to operating running engines. Operating even small-size engines causes potential risk to operator health. The research problem was to identify the acoustic flow field from the front and back of the operating GTM-120 engine. The research was divided into three parts: identify noise at various rotational speeds, identify an acoustic field of the engine, and identify noise level at additional points. Obtained results identify that noise from such a small engine at the range of 67 to 109 dbC is harmful and unpleasant to people who are within a radius of 15 meters from the engine. The final conclusion is that installing mufflers for any size of turbomachinery should be mandatory.

Prediction and optimization of pure electric vehicle tire/road structure-borne noise based on knowledge graph and multi-task ResNet

Pure electric vehicles (PEVs) offer a significant advantage in their lower interior noise levels compared to traditional combustion engines, making them increasingly popular among consumers. However, the absence of engine noise has brought attention to other sources of noise, particularly tire/road structure-borne (TRS) noise, which significantly impacts the sound quality of the PEV interior and passengers’ psychological comfort. To address this issue, numerous studies have employed simulation analysis, data-driven approaches, and experimental test methods for the prediction and optimization of TRS noise. Nevertheless, the optimization and rectification of TRS noise problems present recurring challenges and difficulties due to the involvement of numerous variables and the weak correlation between TRS noise and vibration. In response to these challenges, this paper introduces a novel approach that combines knowledge graph and multi-task ResNet for predicting and optimizing PEV TRS noise. The proposed method has been experimentally validated and has demonstrated superior effectiveness and robustness compared to three commonly used prediction and optimization methods. The paper’s main contributions are twofold: Firstly, it introduces a novel mechanism that fuses mechanism-driven and data-driven approaches by utilizing a knowledge graph to establish dependency and correlation relationships among TRS entities. This fusion, together with the multi-task ResNet, enhances the interpretability of the solution results while improving computational efficiency. Secondly, it introduces a multi-task ResNet prediction method, named TCGC-ResNet, which incorporates a Task-Conditional Gate Control (TCGC) technique. By explicitly separating shared components from task-common components and implementing a gate control mechanism, TCGC-ResNet enables the gradual extraction and separation of task conditional information knowledge. These advancements contribute to a more comprehensive and effective solution for predicting and optimizing PEV TRS noise.

EFFECT OF DIESEL ENGINE DECARBONISATION ON SELECTED EXHAUST VALUES

In this article, the authors refer to the measurement of acoustic emission, where they found that this methodology can determine the combustion characteristics in the combustion chamber of the engine and thus it is possible to confirm a better combustion process and overall reduction of engine noise after decarbonization. The formation of diesel exhaust soot is also supported by the authors' paper, in which they describe how such soot is formed and what effect the addition of special mixtures to the combustion process has on the formation of such carbon deposits. The article uses BG technologies for deep engine decarbonization, including intake and exhaust parts. This technology is one of many on the market, but it was only with this technology that the authors managed to achieve the relevant results that this article will introduce to. For the test, we selected vehicles that had a problem to pass at the emission control.

Investigating consumers’ adoption of electric vehicles: a perceived value-based perspective

PurposeThe current work aims to understand the consumers’ adoption of electric vehicles (two-wheelers) from their value perspective by utilizing the value-based adoption model.Design/methodology/approachThe study considered data from 302 potential electric two-wheeler customers and tested the hypotheses using structural equation modeling.FindingsThe outcomes showed that perceived economic benefits, social image, enjoyable acceleration and enhanced fun and perceived environment (positively) and perceived physical safety risk, perceived cost of ownership and range and charging risk (negatively) influenced the customers’ perceived value linked with electric two-wheeler (ETW) adoption. Only low engine noise emission and infrastructure issues did not affect perceived value.Research limitations/implicationsMost of the respondents considered in the study were less than 35 years old. Hence, the model can be tested for other age groups.Practical implicationsThe study’s outcomes will help ETW marketers, manufacturing companies and governments (state and central) to provide a more convenient environment for electric two-wheelers' adoption and help them curate appropriate strategies.Originality/valueThe current work offers a better understanding of potential customers' ETW adoption by employing a value-based trade-off.

Research on Abnormal Starting Noise of Efficient Engine Caused by Abnormal Combustion Noise

Article Research on Abnormal Starting Noise of Efficient Engine Caused by Abnormal Combustion Noise Feng Huang 1, * , Xiaochun Zeng 1, Xili Wang 1, Xinqi Qiao 2, Yi Wang 1, Liang Fang 3, Yunhua Zhang 3, Dengcheng Liu 4, and Yonghong Deng 4 1 Jiangling Motors Co., Ltd., Nanchang 330001, China 2 Mechanical and Power Engineering College, Shanghai Jiao Tong University, Shanghai 200092, China 3 Automobile College, Tongji University, Shanghai 200092, China 4 Nanchang Automotive Institute of Intelligence & New Energy, Nanchang 330052, China * Correspondence: fhuang6@jmc.com.cn Received: 20 February 2024; Revised: 7 April 2024; Accepted: 9 May 2024; Published: 22 May 2024 Abstract: An abnormal noise problem was reported in a pickup truck equipped with a 2.0 L efficient engine during start-up. The noise was reproduced by the engine anechoic chamber bench for further analysis. NVH test (conducted through audio playback and signal filtering) showed that the noise had a signal of 1–3 kHz. Analysis through cylinder-by-cylinder oil-cutting method found that the abnormal noise completely disappeared after the fourth cylinder was cut off, indicating that the noise was related to the combustion of the fourth cylinder. The combustion characteristics of each cylinder were then analyzed in the starting condition through the Kibox combustion analyzer. The noise appeared after the first fuel injection of the fourth cylinder, and the maximum pressure rise rate was 42 Bar/°, confirming that the abnormal noise was combustion noise. Further tests on the physical fuel injection signal of each cylinder found that the fourth cylinder only had the main injection signal with no pilot injection signal. Further analysis of the synchronization signal recognition method of the model and the underlying logic of the fuel injection program showed that the synchronization signal was recognized for the first time in the starting process after the calculation moment of the 4th cylinder pre-injection program, resulting in the failure of the pre-injection calculation. Finally, the bottom fuel injection interrupt program was pushed back (60° to the crankshaft angle), ensuring smooth injection of the 4th cylinder pilot injection in the starting working condition, thus solving the abnormal noise without impacting engine performance.

Design and Analysis of the Exhaust Muffler for Two-Wheeler Vehicle

The muffler is an essential part of every type of car. On two-wheelers, however, it disperses flue gases throughout the atmosphere and reduces engine noise to a bearable level. It is occasionally called an exhaust muffler or a muffler. A silencer's main function is to block off noise produced when flue gas rushes past it quickly. Furthermore, the exhaust gases ought to be released into the atmosphere. The primary focus of this work is on two-wheeled vehicle exhaust mufflers. The current exhaust silencer design is subjected to CFD and fluid structure interaction analysis with engine-specific boundary conditions. The specifications will guide the creation of the muffler's design. ANSYS, a finite element analysis tool is used to measure and verify exhaust gas temperatures, velocities, and back pressure. Furthermore, a modal analysis is conducted to figure out how a geometric adaptation affects the fundamental frequency of the system. The actual testing consists of fabricating a customized exhaust silencer and evaluating it for noise and back pressure on a two-wheeler engine test rig.

MODELLING AND 3D-PRINTING OF 4-WHEELER MUFFLER (SILENCER)

The primary function of an automobile muffler is to reduce engine noise by dampening the sound produced by the combustion process as it passes through the exhaust system. Mufflers are engineered to meet specific targets for noise reduction and backpressure. Modern exhaust systems are designed to minimize noise and emissions while maximizing durability, safety, flow rates, and cost-effectiveness. Attributes such as vibration, acoustics, thermal distribution, durability, flow, power loss, and integration with the vehicle are also considered in exhaust system design. 3D printing, a form of additive manufacturing, builds three-dimensional objects by depositing successive layers of material. It offers advantages such as speed, affordability, and ease of use compared to other additive manufacturing technologies. The term "3D printing" is increasingly used to encompass all additive manufacturing processes. Its value has been steadily increasing across various sectors, including manufacturing and defense, due to its costeffectiveness, efficiency, and user friendliness. Keywords: Muffler, Noise, 3D Printing, Rapid Prototyping, Design

Underwater radiated noise from small craft in shallow water: Effects of speed and running attitude

Underwater radiated noise from marine vessels is a growing problem, with a large body of evidence now showing the detrimental impact this is having on marine life. Whilst most of the discussion currently focusses on the impact of large vessels, recent evidence shows that small vessels can dominate the soundscape in shallow coastal waters. In this study, acoustic trials are carried out on two small vessels: a pilot boat and a rigid inflatable boat (RIB) across a wide speed range. The average source levels range from 156–173 dB for the pilot boat and 164–166 dB for the RIB (re1μPam). The engine noise is dominant for the RIB across the speed range, and it is further shown that the heading relative to the prevailing waves is a bigger determining factor for noise levels than speed. In upper sea state 3, the overall sound level is up to 6 dB higher when in head waves compared to following waves. Acoustic measurements are combined with onboard vibration measurements to provide further insights into the relationship between noise and vibration. Analysis of this data shows the vessel running attitude plays a key role in determining how much noise is transmitted into the water.

Research on Load Control Of Main Bearing Based on Isfd Principle

The main bearing load of the internal combustion engine is one of the main excitation sources that cause the vibration and radiation noise of the body structure. By reducing the main bearing load transmitted from the main bearing cap to the body, the vibration and noise of the internal combustion engine can be effectively reduced. In this paper, the main bearing cap damper is designed based on the principle of integral squeeze film damper (ISFD), and the load is controlled on the transmission path of the main bearing load. The main bearing load measurement scheme is designed. The load changes of the main bearing cap before and after the installation of the main bearing cap damper are measured experimentally. The load control effect of the main bearing cap damper is analyzed and verified.

Optimisation of Exhaust Emissions, Vibration, and Noise of Unmodified Diesel Engine Fuelled with Canola Biodiesel-Diesel Blends with Natural Gas Addition by Using Response Surface Methodology

The paper presents methods to determine the optimum input parameters of CNG addition, biodiesel blend ratio, and engine speed to improve engine responses in terms of exhaust emissions, vibration, and noise of CNG-biodiesel-diesel fuelled engine. Box-Behnken based on response surface methodology was used to predict and optimise input parameters. Variance analysis was applied to determine the significant relationship between the input parameters and engine responses. At optimum input parameters (CNG addition=9.24 L/min, biodiesel blend ratio=40%, engine speed=1524.24 rpm), the optimum engine responses of NOx, CO, CO2, O2, engine vibration acceleration, and noise were 93.77 ppm, 438.05 ppm, 1.47%, 18.59%, 37.17 m/s2 and 91.34 dB[A], respectively. In terms of coefficient determination of R2, the values were 99.11%, 99.22%, 99.41%, 99.70%, 98.65%, and 98.60% respectively. The correlation between the optimised result and the engine test result showed an acceptable error limit for NOx, CO, CO2, O2, engine vibration acceleration, and noise as 4.2%, 3.8%, 4.9%, 0.25%, 4.12%, and 0.17%, respectively.