Acoustic Leakage Research Articles

Novel AlN/ScAlN composite film SAW for achieving highly sensitive temperature sensors

Traditional SAW devices, typically made from piezoelectric materials like quartz and lithium niobate (LiNbO3), face significant challenges, such as incompatibility with CMOS processes and a decline in piezoelectric performance at high temperatures. Recently, aluminum nitride (AlN) and scandium-doped AlN (ScAlN) have gained attention as promising materials for high-performance SAW devices due to their high acoustic velocity, thermal stability, and CMOS compatibility. However, the low piezoelectric coefficient of AlN and Sc precipitation in ScAlN films limit their broader application. This study investigates the fabrication and optimization of SAW resonators using AlN/ScAlN composite films to enhance piezoelectric performance while mitigating Sc precipitation. A one-port SAW sensor device was designed based on the composite piezoelectric film, and structural optimization was performed by introducing groove structures to further reduce acoustic energy leakage and improve the quality factor (Q). Temperature sensing experiments were conducted using a peripheral oscillator circuit system. The experimental results demonstrated that the developed composite film SAW resonator exhibited excellent phase noise performance and thermal stability within the oscillator circuit, achieving a phase noise of −135.18 dBc/Hz@1 MHz and a frequency temperature coefficient of −31.07 ppm/°C. These findings confirm the potential of the AlN/ScAlN composite film as a reliable and precise temperature sensor.

Study on Acoustic Variability Affected by Upper Ocean Dynamics in South Eastern Arabian Sea

AbstractThe influence of upper ocean dynamics on the acoustic field in the South Eastern Arabian Sea (SEAS) is studied using in situ oceanographic/acoustic measurements from a moored buoy, along with satellite‐derived and climatological data sets. Upper‐ocean variability at the site is quantified using Mixed Layer Depth (MLD), Isothermal Layer Depth (ILD), Barrier Layer Thickness (BLT), Maximum Spice Depth (MSD), and Sonic Layer Depth (SLD), along with surface variability factors such as Sea Surface Temperature, Sea Surface Salinity, Spice, and Sea Level Anomaly. The mixed layer acoustic duct (MLAD) varies from 2 to 100 m, with BLT varying from 5 to 99 m, and a mean SLD of 43 m. A thick transition layer connects the mixed layer with the thermocline during winter. The observations reveal that maximum SLD, MSD, and BLT occurred during January–March. Unlike other seasons when SLD follows MLD, winter SLD is influenced by BLT, suggesting strong salinity stratification due to low‐salinity water intrusion from the Bay of Bengal by East India Coastal Current. During these months, the SLD varies from 80 to 100 m, with the corresponding minimum cut‐off frequency varying from 300 to 200 Hz. Results are correlated with estimated Sound Pressure Level (SPL) from Ambient Noise Measurements during November 2018 to November 2019. SPL variation follows SLD for low and mid‐frequencies, with the highest SPL noted during January‐February. Acoustic propagation simulations at 250 and 1,000 Hz revealed features like acoustic duct leakage and channeling, indicating energy transfers between the surface acoustic duct and deeper layers.

Research on gas pipeline leakage identification based on SE-2DCNN with ultra-weak fiber Bragg grating distributed acoustic sensing system

Abstract A new method for identifying gas pipeline leaks using an ultra-weak fiber Bragg grating (UWFBG) distributed acoustic sensing (DAS) system is proposed. To enhance the accuracy of gas leakage detection, an improved convolutional neural network (CNN) incorporating a two-dimensional structure based on the squeeze-and-excitation (SE) attention mechanism was introduced. The principle of acoustic leakage sensing using this technology is explained in detail, and an experimental setup simulating gas pipeline leaks is constructed. During this process, 9,340 DAS data points across varying leakage volumes and pipeline pressures were collected to create ten distinct datasets. The Mel-frequency cepstral coefficients (MFCC) are employed as the feature input for the optimized SE-2DCNN model, which performs identification tasks. The results show that this optimized model achieves an average leakage identification accuracy of 95.33%, demonstrating superior performance over other methods. This approach offers a robust reference for accurately detecting gas pipeline leakages.

Acoustic emission-based leakage detection for gas safety valves: Leveraging a multi-domain encoding learning algorithm

Safety valves in gas distribution systems are crucial for preventing excessive pressure buildups. Leakage in these valves compromises system stability, causing equipment damage and environmental pollution. Acoustic emission signal analysis from the valve body is essential for detecting leakage, but these signals are often weak and easily disrupted by noise, complicating detection. This paper proposes a multi-domain encoding learning algorithm to address these challenges. Unlike conventional single-domain methods, it combines two encoding images, the Gramian angular difference field and the Markov transition field, to enhance the comprehensive expression of leakage features. A lightweight convolutional neural network reduces computing resource dependency, and a convolutional block attention mechanism improves feature identification. Experimental results demonstrate that our method detects gas leakage with an accuracy of at least 96.77%. It maintains superior performance even under intense noise interference, offering a promising solution for detecting weak gas leakages in safety valves amidst high background noise.

Enhancing stimulated Brillouin scattering in suspended silicon waveguides through subwavelength nanostructuration [Invited

Brillouin optomechanics is playing a key role in the development of groundbreaking devices and novel functionalities in integrated silicon photonics, such as narrow linewidth filtering and lasers, tunable frequency, non-reciprocity, etc. Most silicon-based optomechanical waveguides, which use anchoring arms or perforated slabs to ensure mechanical stability and operate for transverse-electric polarized light, face challenges with acoustic mode leakage into the lateral Si slab, limiting the photon-phonon overlap and the Brillouin gain. Here, we propose new waveguide designs based on subwavelength nanostructuration to tailor near-infrared photons and GHz phonons and maximize the Brillouin gain. We introduce six different geometries suitable for both membrane or fully suspended configurations (i.e., without transversal arms anchoring the core to the Si slab). Our three-dimensional optomechanical simulations predict that subwavelength silicon membranes with strip, slot, and SWG slot core waveguides achieve gains up to 12257 W-1m-1 at mechanical frequencies of 12-13 GHz. Moreover, suspended silicon waveguides with SWG slots achieve a high gain of 43542 W-1m-1 at 4.45 GHz, with the ability to adjust the mechanical frequency from 4 to 9 GHz. Further enhancements in the Brillouin gain are studied by integrating side arms to amplify the moving boundaries effect in the suspended SWG slot waveguides and leveraging the slow light regime, which can significantly increase the Brillouin gain up to 17 × 106 W-1m-1 for a mechanical mode at 11.18 GHz.

Study on the suppression of parasitic resonance of film bulk acoustic wave resonators

To suppress the parasitic resonance of a film bulk acoustic resonator (FBAR) and meet the high-frequency requirements of 5G communication, an FBAR model was established based on COMSOL Multiphysics simulation software. The effects of the piezoelectric materials, electrode lateral size, electrode shape (rectangle, circle, trapezoid, and regular pentagon), and apodization angle (30°, 36°, 40°, and 45°) on parasitic resonance were studied, and the suppression effect of the step-load structure on lateral acoustic wave leakage was discussed. The simulation results show that the best suppression effect on parasitic resonance is achieved when the piezoelectric material is AlN, the electrode shape is a non-regular pentagon, and the apodization angle is 40°; when the resonant area is 3600μm2, its non-circularity is 6.45 %, which is equivalent to the rectangular electrode when the resonant area is 10000μm2. The designed electrode step-load structure improved the quality factor at the parallel resonance point. When the electrode lateral size is 60 μm, the quality factor of the second-order electrode load structure is 1378, which is 10.07 % higher than the quality factor of the electrode-free load structure.

Acoustic emission-based weld crack leakage monitoring via FGI and MCCF-CondenseNet convolutional neural network

Online monitoring of weld crack leakage in pressure pipelines of nuclear power ship based on acoustic emission (AE) technology is of great significance for maintaining the safe and stable operation of the system. However, most of the current leakage studies are conducted through artificially designed pipeline hole types, which deviate from the actual crack morphology and are weakly online, with low identification accuracy and slow monitoring speed. Therefore, a convolutional network of FGI and multi-scale channel information cross fusion based on AE technology is proposed in this paper. First, the FBank feature of the AE signal of pipeline weld leakage are extracted. On this basis, the Gini Index (GI) preference feature is used to filter the useless information in the FBank feature. Then, a multi-scale channel information cross fusion module is designed to improve the feature learning ability of the network through the interaction and fusion of different channel information. Finally, the superiority of the proposed FGI feature extraction method and the effectiveness of the proposed multi-scale channel information cross fusion CondenseNet (MCCF-CondenseNet) convolutional neural network are verified by the pipeline leakage AE monitoring experiments under three crack morphologies. The results show that the identification accuracy of the proposed method is as high as 96.42 %, and the identification speed is significantly faster than other state-of-the-art approaches under the premise of ensuring the identification accuracy. This work provides a new method for the online leakage monitoring of nuclear power pressure pipelines, and has important supporting significance for the online leakage monitoring of other large and complex equipment.

Earmuff performance in the presence of high-level impulse from a recoilless weapon firing in a confined space.

A noise attenuation performance test was conducted on earmuffs using a recoilless weapon launch platform in a confined space, along with two acoustic test fixtures (ATFs). The overpressure at the ATF's effective tympanic membrane comprised direct sound at 185 dB sound pressure level (SPL) and reflected sound at 179 dB SPL. Wearing earmuffs reduced these peaks to 162 dB SPL and 169 dB SPL, respectively. The reflected sound from walls was defined as delayed sound. An analytical model for earmuff noise attenuation simulated their effectiveness. The simulation revealed that when the earmuffs attenuated delayed sound, the acoustic impedance of acoustic leakage and the acoustic impedance of the earmuff material decreased by 96% and 50%, respectively. The negative overpressure zone between direct and delayed sound decreased the earmuffs' fit against the ATF. Additionally, the enclosed volume between the earmuff and the ear canal decreased by 12%. After the installation of bandages on the earmuffs, the overpressure peak of delayed sound was reduced by 5 dB. Furthermore, the acoustic impedance of the earmuff's sound leakage path and the acoustic impedance of the earmuff material deformation path increased by 100% and 809%, respectively.

Advancing deep learning-based acoustic leak detection methods towards application for water distribution systems from a data-centric perspective

Against the backdrop of severe leakage issue in water distribution systems (WDSs), numerous researchers have focused on the development of deep learning-based acoustic leak detection technologies. However, these studies often prioritize model development while neglecting the importance of data. This research explores the impact of data augmentation techniques on enhancing deep learning-based acoustic leak detection methods. Five random transformation-based methods—jittering, scaling, warping, iterated amplitude adjusted Fourier transform (IAAFT), and masking—are proposed. Jittering, scaling, warping, and IAAFT directly process original signals, while masking operating on time-frequency spectrograms. Acoustic signals from a real-world WDS are augmented, and the efficacy is validated using convolutional neural network classifiers to identify the spectrograms of acoustic signals. Results indicate the importance of implementing data augmentation before data splitting to prevent data leakage and overly optimistic outcomes. Among the techniques, IAAFT stands out, significantly increasing data volume and diversity, improving recognition accuracy by over 7%. Masking enhances performance mainly by compelling the classifier to learn global features of the spectrograms. Sequential application of IAAFT and masking further strengthens leak detection performance. Furthermore, when applying a complex model to acoustic leakage detection through transfer learning, data augmentation can also enhance the effectiveness of transfer learning. These findings advance artificial intelligence-driven acoustic leak detection technology from a data-centric perspective towards more mature applications.

Acoustic energy harvesting using phononic crystal fiber with conical input

In this paper, a novel phononic crystal fiber with conical input is introduced for coupling environmental acoustic waves to the fiber core. Environmental acoustic waves can be focused and coupled to the core through conical input. In the first step, a cone shape coupler has been considered for coupling the incident acoustic waves to the core region. This initial idea has a significant acoustic energy loss. This disadvantage encourages us to design a new phononic crystal fiber without it. Designed structure includes a phononic crystal fiber with conical input meaning solid rods have been shaped in conical way at the input section of fiber. By using this structure, environmental acoustic waves can be properly coupled to the core region of the fiber. Acoustic wave leakage to outside of the Phononic crystal fiber has been extremely decreased in comparison with initial coupler. Experimental results indicate that environmental acoustic waves can be focused and coupled to the core region by phononic crystal fiber with conical input.

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Pipeline leakage detection is an integral part of pipeline integrity management. Combining AE (Acoustic Emission) with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold. Finally, pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals and can automatically learn and acquire the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy. The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

Laser-based Covert Channel Attack Using Inaudible Acoustic Leakage from Multilayer Ceramic Capacitors

Laser-based Covert Channel Attack Using Inaudible Acoustic Leakage from Multilayer Ceramic Capacitors

Design of dual-mode resonators based on laterally coupled alternating thickness (LCAT) modes for WiFi 2.4G and n77 dual-passband filter

This paper explores a dual-mode resonator that uses laterally coupled alternating thickness (LCAT) modes, and a dual-passband filter for WiFi 2.4G and n77 bands is constructed. The resonator's frequency can be adjusted by the electrode thickness, period and piezoelectric film thickness, and their effects on the quality factor (Q) values and effective electromechanical coupling coefficient (k2eff) were analyzed. In addition, the impact of Sc-doped concentration on resonators' performance was investigated, and the AlSc0.35N-based resonators exhibited both high Q and k2eff values. Furthermore, two resonant modes were effectively excited by setting two working regions. Adding an acoustic blocking groove reduces mutual acoustic energy leakage, increasing Q values for target modes and suppressing spurious signals. The dual-mode resonator’ structure were further optimized to ensure that both modes have similar high performance. Ultimately, this work constructs a dual passband filter with center frequencies of 2.49 and 3.54 GHz with 75 and 132 MHz bandwidths respectively, and achieves over 60 dB out-of-band rejection.

Enhanced stimulated Brillouin scattering in the unsuspended silicon waveguide assisted with genetic algorithms.

Stimulated Brillouin scattering (SBS), originating from the coupling between optical and acoustic waves, has been widely applied in many fields. Silicon is the most used and important material in micro-electromechanical systems (MEMS) and integrated photonic circuits. However, strong acoustic-optic interaction in silicon requires mechanical release of the silicon core waveguide to avoid acoustic energy leakage into the substrate. This will not only reduce the mechanical stability and thermal conduction, but also increase the difficulties for fabrication and large-area device integration. In this paper, we propose a silicon-aluminium nitride(AlN)-sapphire platform for realizing large SBS gain without suspending the waveguide. AlN is used as a buffer layer to reduce the phonon leakage. This platform can be fabricated via the wafer bonding between silicon and commercial AlN-sapphire wafer. We adopt a full-vectorial model to simulate the SBS gain. Both the material loss and the anchor loss of the silicon are considered. We also apply the genetic algorithm to optimize the waveguide structure. By limiting the maximum etching step number to two, we obtain a simple structure to achieve the SBS gain of 2462 W-1m-1 for forward SBS, which is 8 times larger than the recently reported result in unsuspended silicon waveguide. Our platform can enable Brillouin-related phenomena in centimetre-scale waveguides. Our findings could pave the way toward large-area unreleased opto-mechanics on silicon.

Interference Pattern Anomaly of an Acoustic Field Induced by Bottom Elasticity in Shallow Water

This paper reports the interference pattern of an acoustic field in the 100–300 Hz frequency band observed in a shallow water experiment. Assuming there is a conventional fluid sea bottom, the observed interference pattern is first demonstrated to potentially be abnormal, as a measured waveguide invariant of 1.03 corresponding to the 200–300 Hz frequency band could indicate that the bottom sound speed is relatively high, which is inconsistent with another observation from the interference pattern that the sound field is dominated by only one mode if the frequency is lower than 150 Hz. The numerical analysis shows that, owing to the high bottom sound speed, the third mode can still strongly interfere with the first mode at a frequency of 150 Hz, at least. Accordingly, a dual-layer sea bottom model consisting of a fluid sediment layer and an underlying half-space elastic substrate was proposed to interpret the observed anomaly. The results reveal that the acoustic leakage attenuation due to the elastic substrate plays a major role in the observed dominance of one mode in the sound field occurring below a frequency of 150 Hz. Therefore, the deep bottom’s elasticity may have a significant impact on the interference characteristics of low-frequency acoustic fields in shallow water under certain conditions.

Influencing mechanisms of gas bubbles on propagation characteristics of leakage acoustic waves in gas-liquid two-phase flow

Propagation speed of acoustic waves in liquid can be significantly degraded when gas bubbles exist. To reveal the influence of gas bubbles on propagation characteristics of leakage acoustic waves in liquid pipelines, numerical simulation is utilized in the present paper. It can be divided into three stages when leakage acoustic waves propagate through a single gas bubble and the influence of each stage on propagation characteristics of acoustic waves is discussed. In gas-liquid two-phase flow, there are two basic mechanisms of gas resistance on wave propagation: elastic resistance effect and drag resistance effect. The results show that the influence of elastic resistance effect on sound speed is much greater than that of drag resistance effect. When propagating through a long gas bubble with the radius of 2 mm and the length of 7 mm, the elastic resistance effect causes a time delay of 0.00038 s while the drag resistance effect only causes a time delay of 0.00002 s. The results of this paper contribute to better understanding of the differences in acoustic wave propagation characteristics between different flow patterns in gas-liquid two-phase flow, and lay a foundation for the utilization of acoustic leakage detection methods in gas-liquid two-phase flow pipelines.

Acoustic feature-based leakage event detection in near real-time for large-scale water distribution networks

Abstract Acoustic sensors are widely deployed to detect hidden leakages in water distribution networks (WDNs). However, few studies have been conducted to quantitatively understand the dominant leakage acoustic characteristics, which are usually mixed with unknown environmental noises, coupled with the constraint of sparse deployment of acoustic sensors. In this paper, a comprehensive approach, that performs acoustic data feature analysis, is developed to detect pipe leakages in near real-time via a series of systematic analyses, namely: (1) data quality assessment; (2) features identifications; (3) outlier detection and event classification; and finally (4) near real-time leakage detection. The proposed solution has been tested on two major WDNs in Singapore having around 1,000 km of water pipelines installed with 74 permanently installed hydrophone sensors. The leakage detection results obtained from our case study demonstrate that the dominant leakage acoustic characteristics can be captured in lower intrinsic mode functions (IMFs), to within the frequency range of 100–750 Hz approximately, by decomposing the original acoustic signal. Systemwide leakage event detection and classification models are subsequently trained and tested on acoustic datasets collected over 13 historical months, where more than 70% F1-scores can be obtained from the emulated near real-time leakage detection analysis.

Internet of things acoustic emission for unattended quantitative leakage monitoring

This paper analyzes the theoretical basis of acoustic emission monitoring of leakage in principle. Based on the actual field leakage experiments, the following quantitative relationships are obtained: the quantitative relationship between the amount of leakage and the acoustic emission parameters under the same pressure; the quantitative relationship between different pressures, acoustic emission parameters and leakage rate under the condition of the same valve opening; the characteristic relationship between different leakage openings and acoustic emission parameters under the same pressure difference (tank wall plug). An online experimental demonstration system of leakage rate of water pipes is established. The acoustic emission collector automatically detects the leakage and leakage rates, and transmits the data to the cloud server. After the alarm conditions are set, the alarms will be pushed to the mobile phone if the alarm conditions are met. The cloud server is open for readers to view the results.

Extracting acoustic leakage signals in buried pipes

This paper develops a method for separation and extraction of pipeline leakage signals. The independent component analysis is applied to separate acoustic signals, which is then coupled with the image similarity distance matching fusion algorithm for the extraction of the signals due to leakage. Acoustic signals generated by real pipeline leakage and common interference noise are collected respectively in order to form a multi-channel matrix with random mixing of leakage signal and uncorrected noise signals. It is demonstrated that the suspected leakage signals extracted after re-sequencing match well with the original leakage signal in terms of coherence and peak cross-correlation. They are further verified based on the cumulative energy distribution and the energy ratio at low and high frequencies. By effective separation of leak signals from the uncorrected background noise signals, the method presented in this paper provides the basis for the improvement of pipeline leak localization.

A detection and diagnosis method for tubing leakage below liquid level in gas wellbore

The accurate detection and diagnosis of the position of tubing leakage above or below the liquid level plays an important role in the emergency response to the leakage. This paper presents a new detection and diagnosis method based on acoustic monitoring tubing leakage below the liquid level. The optimized simulation experiment system for tubing leakage was proposed with visualization and controlled flow rate design, and it can capture the annular acoustic characteristics of leakage above and below the liquid level. The leakage identification model is established by optimizing multi-dimensional acoustic characteristic parameters. The results show that when the leakage occurs above and below the liquid level, the distribution of leakage noise in time domain and frequency domain is quite different. Such model can quickly and accurately diagnose whether the leakage of wellbore tubing occurs above or below the liquid level with an accuracy rate of 99.32 %.