Noise Path Research Articles

Investigation of the vibro-acoustic signature of a heat pump through an experimental transfer path analysis

With the increasing interest on heat pumps due to their energy efficiency and environmentally favorable characteristics, concerns regarding their operational noise and vibration levels are becoming paramount, particularly in residential applications where occupant comfort is a priority. A heat pump consists of significantly distinct components regarding their physical properties, contributing to a complex dynamic system with respect to noise and vibration. To better understand its vibro-acoustic signature, this work presents an experimental transfer path analysis (TPA) of an outdoor unit of a heat pump system for residential applications. In this study, the main noise sources and the predominant vibration and noise paths are identified. Predictions of the total and partial responses at the receiver side is determined in order to quantify the contribution of each individual path. For the determination of the operational internal forces, the inversion matrix method is adopted. The findings of this study can contribute to the development of effective design guidelines and engineering solutions for minimizing the acoustic and vibrational footprint of residential heat pumps.

A development on the interior noise prediction technology using active noise control algorithm

When developing NVH of a vehicle, identifying a noise transmission vulnerable path is a very important task. The traditional TPA(Transfer Path Analysis) is based on a physical theoretical equation and calculates the contribution of each path by multiplying the ATF(Acoustic Transfer Function) of each path by the driving load. In this case, it is often difficult to calculate the driving load, and M/H consumption is also high. OTPA(Operational Transfer Path Analysis) performs transfer path analysis only with driving signals for each route, but has a disadvantage of insufficient physical causality. In this study, we proposed an efficient indoor noise TPA method that modifies the ANC algorithm by combining the reference signals while driving with the secondary path of Filtered-X LMS. The proposed method uses the secondary transfer function of Fx-LMS as the ATF for each path. It is not necessary to obtain physical load by predicting target noise with the Ref signal for each route. Therefore, it is possible to predict indoor noise with low M/H. In this study, it can be confirmed that the proposed method shows similar results compared to the existing methods and efficiently identifies main noise path for interior noise.

The consideration of a simplified method for predicting aircraft noise

The Japanese segment model focuses on understanding noise impacts in the surrounding areas relatively close to the airport. The basic data to be prepared for predicting are usually the results of measurements and analysis in the area near the airport. In areas farther away from airports, the altitude of aircraft is higher, there is an issue that prediction results based on the basic data collected near airports accurately reflect the reality. Often, areas far from airports get complaints even if noise exposure was not high. There is a desire for a model that can predict with relatively high accuracy and ease even in locations far from airports like this. Based on this background, we have developed a simple prediction model based on actual observation results of noise and flight paths, which can be used as basic data for aircraft noise prediction. In this approach, a database is created using noise measurements obtained from unattended noise monitoring and short-term survey results and aircraft position information from the ADSB. In noise propagation calculations, predictions are made considering factors such as lateral attenuation of ground surfaces, air absorption, and variability in flight paths by a simple method.

An innovative approach for enclosure design of power generators to meet noise regulation targets

The use of diesel-powered engine generators as a power source is very common for various industrial and residential applications. There are strict regulatory requirements and legislations to limit the noise levels from these generators below statutory limits. The purpose of the paper is to present an approach using statistical energy analysis (SEA) augmented by experimental inputs for the prediction of noise levels for the generators and propose strategies for optimized enclosure design that effectively mitigate noise to achieve regulatory requirements. The study begins by developing an SEA model that represents the acoustic behavior of the generator and enclosure. The airborne sources such as engine, alternator, radiator fan and exhaust are modelled explicitly using experimental noise source characterization. Based on the established SEA model and excitation sources, noise levels are predicted through the calculation of energy flow between subsystems. The predicted sound levels for an existing Diesel Generator with enclosure were validated with its test data to ensure correctness of SEA model. Parametric studies were carried out using simulations on key variables, including critical noise paths, panel thickness, and noise control treatments. These investigations helped identify optimal process parameters that reduce noise levels emitted by genset in adherence to noise regulations.

Investigation of noise characteristics of electric vibrator utilizing acoustic camera and transfer path analysis

Abstract Mechanical equipment operation frequently results in noise generation, which can raise concerns regarding quality and safety. This study focuses on electric vibrators, essential electromechanical systems, with the goal of identifying the source and transfer path of noise. To achieve this objective, an acoustic camera positioning analysis was performed to approximate the distribution and characteristics of the noise. Building upon this foundation, a thorough evaluation of the transfer path and noise contribution was conducted. Subsequently, targeted noise suppression methods were implemented based on the properties of the noise. Primary noise sources were observed to be classified into wind turbulence and mechanical vibration based on operating conditions. Time-frequency analysis revealed the presence of low-frequency signals (around 50 Hz) and intermediate-frequency signals (around the resonant frequency), corresponding to wind-induced and mechanical response noise, respectively. Leveraging the characteristics of the noise and its transfer path, passive methods (Sound-absorbing rubber and Soundproof cotton) and active method (FxLMS algorithm) were employed. The findings of this study contribute to a better understanding of noise mechanisms and provide insights for noise emission strategies.

Evaluation of LoRa Network Performance for Water Quality Monitoring Systems

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes.

A novel adaptive blind deconvolution algorithm: application to feature extraction of weak faults in RV reducer gears

Abstract Aiming at the problem that the non-stationary and nonlinear weak fault signal of RV (rotate vector) reducers is hard to extract fault features due to the influence of noise and transmission paths, as well as the selection of parameters for maximum correlation kurtosis deconvolution (MCKD) relies heavily on manual experience, this article proposes a fault feature extraction method based on parameter adaptive MCKD for the gear faults of RV reducers. Firstly, the sparrow search algorithm combining sine-cosine and Cauchy mutation (SCSSA) is used to adaptively search for the input parameters of MCKD and obtain the signal after deconvolution with the optimal parameters. Secondly, the deconvoluted signal is subjected to ensemble empirical mode decomposition to obtain modal components on different frequency bands. Finally, calculate the multi-scale fuzzy entropy (MFE) of each component, constructing a MFE feature set vector, and input the feature vector into the support vector machine for fault classification and recognition. The experimental analysis and verification results both indicate that the proposed method can adaptively enhance the weak impact components in the gear signals of the RV reducer, effectively extracting weak fault features disturbed by noise. Compared with minimum entropy deconvolution, multipoint optimal minimum entropy deconvolution adjusted and MCKD, the proposed method has improved identification rate by 17.50%, 10.63% and 15.63%, respectively. In addition, in comparison to multiverse optimization and particle swarm optimizatio algorithms, the SCSSA exhibits superior performance when optimizing MCKD parameters, offering faster convergence speed, higher accuracy, and greater robustness.

Performance analysis of two-way multi-users cooperative communication system based on GSPIM-DCSK scheme

Based on the Joint Grouping Subcarrier-Permutation Index Modulation-DCSK (GSPIM-DCSK) system, we provide a new two-way multi-user half-duplex cooperative communication system in this work. Given its many benefits, the GSPIM-DCSK modulation is specifically selected as a choice for network coding techniques. A permutation index is used in the GSPIM-DCSK system to incorporate additional data bits with the modulating and carrier index bits. Establishing a cooperative multiuser system where users attempt to make contact with one another over a common relay is the main goal of this effort. Multi-user nodes and a single relay with a cooperative operator on staff for decoding and forwarding make up this system. To avoid interference from other users, the user’s nodes multiplex across various time intervals. We derive analytical expressions for the average bit error rate (BER) of the GSPIM-DCSK multi-user cooperative communication system in the presence of additive white Gaussian noise (AWGN) and multiple paths Rayleigh fading channels. The analytical and simulated average BER findings have been contrasted to verify the accurateness of the average BER analysis. Additionally, a comparison is presented between the average bit error rate (BER) performance of a novel GSPIM-DCSK cooperative system and an established DCSK cooperative system. The throughput and link spectral efficiency of this novel cooperative system are analyzed and compared with those of existing traditional cooperative systems. For the two-way two-user cooperative system, this comparison is done with two-way two-user systems like DCSK-CC and one-way cooperative system based on CIM-SR-DCSK-CC; for the two-way three-user cooperative system, it is done with scheme1 and scheme2. To compare and assess the performance of various systems, simulation results are employed. Furthermore, an analysis demonstrates how the proposed cooperative system, when combined with GSPIM-DCSK techniques, improves the efficiency of conventional cooperative DCSK systems.

Time delay interferometry combination with zero-response

In space-based gravitational wave detection, gravitational wave signals are detected by measuring the Doppler frequency shift of laser exchange between three satellites. Although the laser is pre-stabilized, the laser phase noise is significantly higher than the gravitational wave signal, approximately 7–8 orders of magnitude higher. To reduce the otherwise-overwhelming laser phase noise, the time delay interferometry (TDI) technique is employed by properly time-shifting and linearly combining raw data. Different TDI combinations have various abilities in suppressing laser phase noise, and the response function of gravitational wave signal as well as the power spectral density of noise floor (including test mass acceleration noise and optical path noise) are also different. In this work, we present a unique class of TDI combinations, denoted as [ZX]116,[ZX]216,[ZX]316,[ZX]416, and [ZX]516, which exhibit a distinct geometric space–time diagrams composed of Michelson interferometers as the fundamental constituents. Specifically, when the detector arm lengths are assumed to be equal, these combinations yield response functions and noise power spectral densities of zero. To evaluate the detection performance of these combinations, we investigate their response functions and noise power spectral densities when the detector arm lengths are unequal, and furthermore, derive the analytical expressions for their sensitivity functions. Through our analysis, we have discovered some intriguing results. When the detector arm lengths are unequal, both the response functions and noise spectral densities of these combinations are correlated with the difference in arm lengths L3−L2. However, the sensitivity functions of these TDI combinations remain unaffected by the specific values of the arm length difference, and are consistent with the sensitivity functions of widely-used TDI combinations. For instance, the sensitivity function of [ZX]116 combination is the same as that of the Michelson combination, while the sensitivity functions of [ZX]216 and [ZX]416 combinations match with that of the Sagnac combination. Furthermore, the [ZX]316 and [ZX]516 combinations share the same sensitivity function as the fully symmetric Sagnac combination, enabling the identification of instrument noise and stochastic gravitational wave backgrounds. The discovery of this class of combinations provides support for the diversity of data processing.

Researches advanced in path planning to indoor fire escape and rescue based on SLAM

Helping trapped people understand the external situation and provide navigation to escape the fire is the key to reducing fire casualties. Thanks to the rapid development of artificial intelligence technology, the combination of simultaneous localization and mapping (SLAM) and path planning technology has gradually become a new research hotspot, which can help provide on-site fire information, maps and navigation for trapped people and firefighters. However, improving the accuracy of SLAM techniques under harsh conditions (e.g., thick smoke, high temperature) is still an open topic. Focusing on SLAM noise reduction and path planning, in this paper, we detail the latest research progress of SLAM technology in fire escape assistance. Specifically, we first introduce the current development and application frontiers of SLAM technology, and then analyze and compare the application of SLAM technology in fire scenarios. In addition, the performance changes of the two continuous A* algorithms and the RRT algorithm during global path planning for fire scenarios are compared. Finally, we discuss the development trend of SLAM in future fire escape and rescue.

Experimental Study of Radiated Emission Due to Secondary Common-Mode Current in Buck Converters

High switching frequency and fast switching speed of wide bandgap power semiconductor device-based power converters may increase radiated emissions more than conventional silicon-power device-based power converters. Based on the background, this article experimentally investigates radiated emission sources in buck converters. The experimental system constructed in this article has two common-mode noise paths, the primary common-mode and the secondary common-mode. First, this article outlines that a secondary common-mode voltage source is caused by a primary common-mode current and an imbalance of power supply side impedances, and the secondary common-mode current path acts as a monopole antenna. The common-mode currents and the radiated emissions are measured in an anechoic chamber when the common-mode inductor for each mode is connected. The measurement results show that the primary common-mode current generates the secondary common-mode noise, and the secondary common-mode noise is the dominant source of radiated emission in the experimental system.

Empowering A* Algorithm With Neuralized Variational Heuristics for Fastest Route Recommendation

Fastest route recommendation (FRR) is crucial for intelligent transportation systems. The existing methods treat it as a pathfinding problem on dynamic graphs, and extend A* algorithm with neuralized travel time estimators as cost functions. However, they fail to provide effective heuristic cost due to the neglect of its admissibility and the utilization of noise path information, resulting in sub-optimal results and inefficiency. Besides, path sequentiality is also ignored, affecting algorithm accuracy as well. In this paper, we propose a variational inference based fastest route recommendation method, which follows the framework of A* algorithm and provides effective costs for routing. Specifically, we first adopt a sequential estimator to accurately estimate the travel time of a specific path. More importantly, we design a variational inference based estimator, which models the distribution of travel time between two nodes and provides an effective heuristic cost with high probability of being admissible. We further take advantage of adversarial learning to enrich the fastest path information. To the best of our knowledge, we are the first to use variational estimator to consider the admissibility of heuristics in FRR. Extensive experiments are conducted on two real-world datasets. The results verify the performance advantage of our proposed method.

Active electric machine control to reduce compressor noise in heat pumps

As heat pumps help to reduce CO2 emission in residential heating, rapidly growing heat pump sales are predicted. In order to install heat pumps in densely populated residential areas, they must comply with strict noise requirements. Due to advances in control technology in recent years, it seemed promising to investigate the noise reduction potential of a heat pump by means of optimized control. An active control strategy called Current Shaping (CS) has been applied to reduce compressor induced heat pump noise by applying suitable currents to the compressors electric machine. For this purpose, by CS we superimposed suitable current harmonics on the almost constant Iq driving current. In this article we focus on how these modified driving currents affect the noise emitted from the compressor and how this reduces heat pump noise. A compressor test bench was built to perform the CS optimization procedure and to determine noise optimal currents by minimizing compressor vibration. An important finding of this investigation is that the compressor vibrations can be efficiently reduced by CS, and hence also the noise emissions. The vibration reduction potential depends on the compressor harmonic and the operating condition. Additionally, pressure pulsations were measured within the refrigerant pipes and found not to be affected by CS. After the test bench optimization, we applied the optimized currents to a heat pump in a sound chamber to proof that these optimized currents efficiently reduce heat pump noise. For the investigated frequency range of up to 400 Hz our investigations show the three following: First, active electric machine control CS is a suitable method to reduce heat pump noise. Second, the test bench, presented in this paper is suitable to determine the CS current harmonics to reduce heat pump noise. Third, it can be confirmed that the main heat pump noise source is compressor vibration. In general, the two main noise sources of a heat pump are the fan and the compressor. For the compressor there are three noise paths, namely structure-borne and air-borne noise, both excited by compressor vibration, and fluid-borne noise, excited by pressure pulsations in the refrigerant in the piping system. Therefore, from the investigations presented here, it can be derived that the compressor noise is dominating heat pump noise. Moreover, compressor vibrations are dominating heat pump noise and not pressure pulsations in the piping system.

Improvement of high-power thyristor trigger circuit with switching-mode power supply for differential-mode noise reduction

This paper focuses on the differential-mode (DM) noise of high-power thyristor (HPT) trigger circuit with switching-mode power supply (SMPS) in capacitive pulsed power supply (CPPS). Parasitic parameter models of components are discussed and analyzed. Based on the parasitic parameter models of components, a DM noise prediction model of HPT trigger circuit with SMPS is developed. The generation mechanism and propagation paths of the DM noise in HPT trigger circuit with SMPS are investigated. To reduce the DM noise, a capacitive SMPS technique is adopted in the HPT trigger circuit with SMPS. Simulation results validate the theoretical analysis.

Common pitfalls and potential solutions for crypto-currency mining facility noise

Crypto-currency mining facilities have become more and more common over the last few years, but there is no standard for construction or noise mitigation for these facilities. Many noise issues are encountered after the facilities are complete and operating. The facilities can contain thousands of "miners" which can be quite loud. Often, the manufacturers do not supply reliable sound data and the miners themselves are constantly evolving. The miners have extreme power and ventilation requirements which introduce additional noise sources and paths, as well as site requirements. I have examined the effectiveness of mitigation techniques for these facilities and addressed potential pitfalls for facilities based on similar issues observed across several projects.

Experimental Methodology to Characterize the Noise Paths in a Horizontal-Axis Washing Machine

In this paper, an experimental methodology to characterize the noise paths in a washing machine with a horizontal axis was developed. The noise paths considered in this research were the noise that escapes through holes, the non-resonant path through the panels, and the noise radiated by the panels of the cabinet. The characterization method was based on several sound intensity measurements on the outside panels of the washing machine. In addition to these measurements, characterization of the radiation factor was performed by applying a method that relates intensity and vibration measurements while the structure of the washing machine is excited using a shaker. Applying the methodology to a washing machine, the main transmission path of the noise along the frequency domain where this home appliance has its highest values was identified. This methodology can provide the manufacturer with a guide to improve the acoustic performance of washing machines by applying noise control solutions in the noise path depending on the frequency domain.

Pathwise regularisation of singular interacting particle systems and their mean field limits

We investigate the regularising effect of certain perturbations by noise in singular interacting particle systems under the mean field scaling. In particular, we show that the addition of a suitably irregular path can regularise these dynamics and we recover the McKean–Vlasov limit under very broad assumptions on the interaction kernel; only requiring it to be controlled in a possibly distributional Besov space. In the particle system we include two sources of randomness, a common noise path Z which regularises the dynamics and a family of idiosyncratic noises, which we only assume to converge in mean field scaling to a representative noise in the McKean–Vlasov equation.

A Study on the Noise Optimization through the Analysis of Electric Vehicle Noise Paths

Since an internal combustion engine is not required for electric vehicle, the electric vehicle's noise is much lower than that of conventional internal combustion engine based vehicle. However, a road noise and wind noise that used to be masked by engine noise became the critical noise factors. Moreover, the electric vehicle generates a specific current conversion noise and motor high frequency noise that provides an unpleasant sound environment for drivers and passengers. In this study, therefore, the noise path of the electric vehicle was identified through Power Based Noise Reduction (PBNR) and Acoustic Transfer Function (ATF) tests. As a result of analyzing the noise path, it was confirmed that the rear side of vehicle was weak in terms of noise. As a countermeasure against this, it was confirmed that interior noise level could be improved by reinforcing the luggage side.

Sound Insulation Property of Waste Jute Fiber/Recycled High-Density Polyethene Composites

Noise pollution is a potential threat to human health and the environment. Using acoustic materials (barriers/insulators and absorbers) in the noise path is a customarily approach for noise control. Nowadays, compared to traditional sound insulating materials (metals, gypsum and concrete), polymers (plastics) have gained more interest among researchers because of their unique features like lightweight, ease of design, low cost, and available state-of-the-art technologies for mass production. Concurrently, the massive amounts of solid waste such as plastic, e-waste, garment waste (textile) and agro residue end up in landfills or are incinerated. Disposal of these solid wastes through landfilling or burning in the air deteriorates the environment. Therefore, in the context of these problems, the present research aims to prepare sound-insulating material by utilizing waste jute fibre (WJF) and recycled plastic (high-density polyethene-rHDPE) through compression moulding. The effect of WJF weight percentage on sound insulation property was studied. The prepared composites were thoroughly characterized for their properties related to sound insulation, i.e., density and tensile strength. The sound transmission loss of the composites was measured as per ASTM-E2611 using a four-microphone impedance tube setup. It was found that with the increase in WJF amount, the sound transmission loss was significantly improved. Moreover, the measured experimental results were compared with the theoretical results computed from mass and stiffness law equations, which showed a good agreement.

Model Parameter Self-Correcting Fuzzy Proportional–Integral–Derivative Adaptive Control of the Integration Time of Raman Spectrometers

The control of the integration time of Raman spectrometer is a crucial factor for the acquisition quality of Raman spectrum. However, due to the coupling effect of linear and nonlinear factors such as the intensity of Raman scattering effect, acquisition noise, fluorescence effect, and spectrometer optical path, it is rather difficult to fast control the integration time, which influences the acquisition efficiency. In this article, aiming at the nonlinear automatic optimal control of the integration time, a model parameter self-correcting fuzzy proportional–integral–derivative adaptive (MPSC-FPIDA) control method is proposed. By constructing an initial parameter prediction model and the fuzzy PID controller, two processing stages, namely, Raman spectrum intensity detection and fuzzy PID iterative control, are carried out, with which the automatic calculation and optimal control of the integration time of the Raman spectrometer are achieved according to the set point of spectrum intensity or signal-to-noise ratio (SNR). By introducing a parameter self-correcting factor of the initial model, the adaptive correction of the parameters of the initial prediction model of Raman spectrometers with different optical laser wavelengths is realized, which ensures the universality of this method to spectrometers of different types. The experiment demonstrates that when it comes to acquisition of Raman spectrum for various samples, the method proposed in this article can accurately calculate the optimal integration time within 2–3 iterations and automatically adapt to the performance parameters of different Raman spectrometers, of which the control performance is as good as that of modeling spectrometers. This shows that the method features fast convergence speed, strong robustness, and versatility. In addition, compared with commonly used linear control methods, it can effectively suppress the coupling effect of linear and nonlinear factors, which improves the efficiency of spectrum acquisition and enhances the control performance by over 50% as well.