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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Acoustic Properties of Aerogels: Current Status and Prospects

Noise reduction remains an important priority in the modern society, in particular, for urban areas and highly populated cities. Insulation of buildings and transport systems such as cars, trains, and airplanes has accelerated the need to develop advanced materials. Various porous materials, such as commercially available foams and granular and fibrous materials, are commonly used for sound mitigating applications. In this review, a special class of advanced porous materials, aerogels, is examined, and an overview of the current experimental and theoretical status of their acoustic properties is provided. Aerogels can be composed of inorganic matter, synthetic or natural polymers, as well as organic/inorganic composites and hybrids. Aerogels are highly porous nanostructured materials with a large number of meso‐ and small macropores; the mechanisms of sound absorption partly differ from those of traditional porous absorbers possessing large macropores. The understanding of the acoustic properties of aerogels is far from being complete, and experimental results remain scattered. It is demonstrated that the structure of the aerogel provides a complex three‐dimensional architecture ideally suited for promising high‐performance materials for acoustic mitigation systems. This is in addition to the numerous other desirable properties that include low density, low thermal conductivity, and low refractive index.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Energy dissipation via acoustic emission in ductile crack initiation

In this article, we present a modeling approach to estimate the energy release due to ductile crack initiation in conjunction to the energy dissipation associated with the formation and propagation of transient stress waves typically referred to as acoustic emission. To achieve this goal, a ductile fracture problem is investigated computationally using the finite element method based on a compact tension geometry under Mode I loading conditions. To quantify the energy dissipation associated with acoustic emission, a crack increment is produced given a pre-determined notch size in a 3D cohesive-based extended finite element model. The computational modeling methodology consists of defining a damage initiation state from static simulations and linking such state to a dynamic formulation used to evaluate wave propagation and related energy redistribution effects. The model relies on a custom traction separation law constructed using full field deformation measurements obtained experimentally using the digital image correlation method. The amount of energy release due to the investigated first crack increment is evaluated through three different approaches both for verification purposes and to produce an estimate of the portion of the energy that radiates away from the crack source in the form of transient waves. The results presented herein propose an upper bound for the energy dissipation associated to acoustic emission, which could assist the interpretation and implementation of relevant nondestructive evaluation methods and the further enrichment of the understanding of effects associated with fracture.

42 ENGINEERING↗
DOE OSTI2019

In Situ Quality Monitoring in AM Using Acoustic Emission: A Reinforcement Learning Approach

Additive manufacturing (AM) has attracted considerable attention in recent years. This technology overcomes the geometrical limits of workpieces produced with the traditional subtractive methods and so gives the opportunity to manufacture highly complex shapes. Unfortunately, the repeatability of the manufacturing process and the monitoring of quality are not reliable enough to be utilized in mass production. The quality monitoring of AM processes in commercial equipment has been largely based on temperature measurements of the process zone or high-resolution imaging of the layers. However, both techniques lack information about the physical phenomena taking place in the depth of the materials medium and this limits their reliability in real-life applications. To overcome those restrictions, we propose to combine acoustic emission and reinforcement learning. The former captures the information about the subsurface dynamics of the process. The latter is a branch of machine learning that allows interpreting the received data in terms of quality. The combination of both is an original method for in situ and real-time quality monitoring. Acoustic data were collected during a real process using a commercial AM machine. The process parameters were selected to achieve three levels of quality in terms of porosity concentration while manufacturing a stainless steel 316L cuboid shape. Using our method, we demonstrated that each level of quality produced unique acoustic signatures during the build that were recognized by the classifier. The classification accuracy reached in this work proves that the proposed method has high potential to be used as in situ and real-time monitoring of AM quality.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Exploiting Acoustic Anisotropy to Detect Recrystallization in a Ni Single Crystal Using Ultrasonic Nondestructive Inspection

Ultrasonic inspection was used to detect regions of recrystallization (RX) within a single crystal of Ni. RX can produce polycrystalline regions within single-crystal (SX) materials. Because RX regions act as initiator sites for failure by fatigue-crack growth in otherwise SX Ni-based superalloys, RX is an issue of direct concern for the service life of high-performance turbine blades. Detection and discrimination of RX regions in single-crystal materials are significant challenges for current nondestructive inspection technologies. The present study leverages the elastic anisotropy of single crystals, which produces acoustic anisotropy, to ultrasonically detect and characterize RX regions produced within single-crystal specimens. Single crystals of pure Ni were processed to create controlled regions of RX. These specimens were ultrasonically examined in an immersion tank using a high-resolution, polyvinylidene fluoride transducer to detect acoustic impedance changes across the boundaries between different crystals (i.e., grains) within the RX region. Data from time-of-flight and absolute peak amplitude measurements were used to create acoustic images that were compared against electron microscope images of the RX regions. The ultrasonic inspection technique described here successfully detected not only the locations of RX regions, but also the approximate sizes and shapes of the RX regions.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for characterizing radiated noise from marine energy converters

Marine energy converters can generate electricity from energetic ocean waves and water currents. Because sound is extensively used by marine animals, the radiated noise from these systems is of regulatory interest. However, the energetic nature of these locations poses challenges for performing accurate passive acoustic measurements, particularly with stationary platforms. The Drifting Acoustic Instrumentation SYstem (DAISY) is a modular hydrophone recording system purpose-built for marine energy environments. Using a flow shield in currents and mass–spring–damper suspension system in waves, we demonstrate that DAISYs can effectively minimize the masking effect of flow noise at frequencies down to 10 Hz. In addition, we show that groups of DAISYs can utilize time-delay-of-arrival post-processing to attribute radiated noise to a specific source. Consequently, DAISYs can rapidly measure radiated noise at all frequencies of interest for prototype marine energy converters. Furthermore, the resulting information from future operational deployments should support regulatory decision-making and allow technology developers to make design adjustments that minimize the potential for acoustic impacts as their systems are scaled up for utility-scale power generation.

16 TIDAL AND WAVE POWER↗
DOE OSTI2017

An acoustic-convective splitting-based approach for the Kapila two-phase flow model

In this paper we propose a new acoustic-convective splitting-based numerical scheme for the Kapila five-equation two-phase flow model. The splitting operator decouples the acoustic waves and convective waves. The resulting two submodels are alternately numerically solved to approximate the solution of the entire model. The Lagrangian form of the acoustic submodel is numerically solved using an HLLC-type Riemann solver whereas the convective part is approximated with an upwind scheme. The result is a simple method which allows for a general equation of state. Numerical computations are performed for standard two-phase shock tube problems. A comparison is made with a non-splitting approach. The results are in good agreement with reference results and exact solutions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

Bayesian seismic inversion based on rock-physics prior modeling for the joint estimation of acoustic impedance, porosity and lithofacies

We propose a Bayesian approach for seismic inversion to estimate acoustic impedance, porosity and lithofacies within the reservoir conditioned to post-stack seismic and well data. The link between elastic and petrophysical properties is given by a joint prior distribution for the logarithm of impedance and porosity, based on a rock-physics model. The well conditioning is performed through a background model obtained by well log interpolation. Two different approaches are presented: in the first approach, the prior is defined by a single Gaussian distribution, whereas in the second approach it is defined by a Gaussian mixture to represent the well data multimodal distribution and link the Gaussian components to different geological lithofacies. The forward model is based on a linearized convolutional model. For the single Gaussian case, we obtain an analytical expression for the posterior distribution, resulting in a fast algorithm to compute the solution of the inverse problem, i.e. the posterior distribution of acoustic impedance and porosity as well as the facies probability given the observed data. For the Gaussian mixture prior, it is not possible to obtain the distributions analytically, hence we propose a Gibbs algorithm to perform the posterior sampling and obtain several reservoir model realizations, allowing an uncertainty analysis of the estimated properties and lithofacies. Both methodologies are applied to a real seismic dataset with three wells to obtain 3D models of acoustic impedance, porosity and lithofacies. The methodologies are validated through a blind well test and compared to a standard Bayesian inversion approach. Using the probability of the reservoir lithofacies, we also compute a 3D isosurface probability model of the main oil reservoir in the studied field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Optimal design of acoustic metamaterial cloaks under uncertainty

In this work, we consider the problem of optimal design of an acoustic cloak under uncertainty and develop scalable approximation and optimization methods to solve this problem. The design variable is taken as an infinite-dimensional spatially-varying field that represents the material property, while an additive infinite-dimensional random field represents the variability of the material property or the manufacturing error. Discretization of this optimal design problem results in high-dimensional design variables and uncertain parameters. To solve this problem, we develop a computational approach based on a Taylor approximation and an approximate Newton method for optimization, which is based on a Hessian derived at the mean of the random field. We show our approach is scalable with respect to the dimension of both the design variables and uncertain parameters, in the sense that the necessary number of acoustic wave propagations is essentially independent of these dimensions, for numerical experiments with up to one million design variables and half a million uncertain parameters. Additionally, we demonstrate that, using our computational approach, an optimal design of the acoustic cloak that is robust to material uncertainty is achieved in a tractable manner. The optimal design under uncertainty problem is posed and solved for the classical circular obstacle surrounded by a ring-shaped cloaking region, subjected to both a single-direction single-frequency incident wave and multiple-direction multiple-frequency incident waves. Finally, we apply the method to a deterministic large-scale optimal cloaking problem with complex geometry, to demonstrate that the approximate Newton method’s Hessian computation is viable for large, complex problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2017

An experimental comparison of various methods of nearfield acoustic holography

An experimental comparison of four different methods of nearfield acoustic holography (NAH) is presented in this study for planar acoustic sources. The four NAH methods considered in this study are based on: (1) spatial Fourier transform, (2) equivalent sources model, (3) boundary element methods and (4) statistically optimized NAH. Two dimensional measurements were obtained at different distances in front of a tonal sound source and the NAH methods were used to reconstruct the sound field at the source surface. Reconstructed particle velocity and acoustic pressure fields presented in this study showed that the equivalent sources model based algorithm along with Tikhonov regularization provided the best localization of the sources. Reconstruction errors were found to be smaller for the equivalent sources model based algorithm and the statistically optimized NAH algorithm. Effect of hologram distance on the performance of various algorithms is discussed in detail. The study also compares the computational time required by each algorithm to complete the comparison. Four different regularization parameter choice methods were compared. The L-curve method provided more accurate reconstructions than the generalized cross validation and the Morozov discrepancy principle. Finally, the performance of fixed parameter regularization was comparable to that of the L-curve method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Data-driven acoustic measurement of moisture content in flowing biomass

Measuring the moisture content in flowing biomass is critical to processes such as liquid biofuel conversion, such as biogasoline, biodiesel, bio jet kerosene, etc. However, biomass tends to flow in aggregates, which results in significant inhomogeneities in the amount of biomass flowing in front of a sensor at a given time, and there can be significant overlap in the material properties of dry vs wet biomass, leading to poor signal-to-noise ratio. We present a technique for identifying biomass moisture content using a series of acoustic pitch-catch measurements to quantify the sound speed and acoustic amplitude through the biomass, in conjunction with classical machine learning techniques, including Naive Bayes, Random Forest, and K-Nearest Neighbors classification. We amplify the differences between the acoustic measurements in different moisture levels by collecting a series of pulse-echo measurements, which we sort in order of ascending sound speed. We test the accuracy of the technique on experimentally-prepared batches of corn stover biomass with specified moisture levels and measure the average error in the estimated moisture level as a function of the number of pitch-catch measurements used. We observe average estimation errors as low as 6.7% by increasing the number of measurements and optimizing the hyperparameters. This work presents a novel method determining moisture content in flowing biomass with inhomogeneous flow. Additionally, this technique has application in optimizing biomass conversion processes, as well as other fields including, paper production, natural fiber processing, and mineral extraction.

09 BIOMASS FUELS↗
DOE OSTI2019

Investigation of the nonlinearity of transducer acoustic couplants for nonlinear elastic measurements

The study of nonlinear elasticity of materials often relies on attaching contact transducers to a sample to achieve the required strain amplitudes. When measuring and quantifying the degree of nonlinearity, it is assumed that the nonlinearity of the bond between the material and the transducer is negligible. We aim to characterize the nonlinearity of common acoustic couplants used in nonlinear measurements. Other factors are also reported since bond nonlinearity is not the only important property when choosing an acoustic couplant, i.e., amount of energy transfer, thermal properties, cure time, toxicity, and ease of application and/or removal. Various types of acoustic couplants including, epoxies, glues, thermal adhesives, and coupling agents were studied using Nonlinear Resonance Ultrasound Spectroscopy (NRUS), which is a measure of the hysteretic nonlinearity of a material. Measurements were made on cylindrical samples of acrylic, aluminum alloy 6061-T651, and Crab Orchard sandstone, chosen for their range of sound speeds, attenuation, and nonlinearity.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Time-domain Brillouin scattering theory for probe light and acoustic beams propagating at an angle and acousto-optic interaction at material interfaces

A theory has been developed to interpret time-domain Brillouin scattering (TDBS) experiments involving coherent acoustic pulse (CAP) and light pulse beams propagating at an angle to each other. It predicts the influence of the directivity pattern of their acousto-optic interaction on TDBS signals when heterodyne detection of acoustically scattered light is in backward direction to incident light. The theory reveals relationships between the carrier frequency, amplitude and duration of acoustically induced ”wave packets” in light transient reflectivity signals, and factors such as CAP duration, widths of light and sound beams, and their interaction angle. It describes the transient dynamics of these wave packets when the light and CAP encounter material interfaces, and how the light scattering by the incident CAP transforms into scattering by the reflected and transmitted CAPs. The theory suggests that single-point TDBS experiments can determine not only depth positions of buried interfaces but also their local inclinations/orientations.

42 ENGINEERING↗
DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗
DOE OSTI2017

Comparing the survival rate of juvenile Chinook salmon migrating through hydropower systems using injectable and surgical acoustic transmitters

Acoustic telemetry is one of the primary technologies for studying the behavior and survival of fishes throughout the world. The size and performance of the transmitters is still the key limiting factor despite that considerable effort has been expended to understand the biological effects of implantation of acoustic transmitters in yearling and subyearling Chinook salmon. The newly developed injectable transmitter is the first active acoustic tag that can be implanted via injection instead of surgery. It also lasts more than four times longer than the commercially-available transmitters. A two-part field study was conducted to evaluate the performance of the injectable transmitter and its effect on the survival of implanted fish. The injectable transmitter performed well and similarly to the other commercially-available transmitters tested. Snake River subyearling Chinook salmon smolts implanted with the injectable tag had a higher survival probability from release to each of 11 downstream detection arrays than concurrent releases of fish surgically implanted with commercially-available tags. In addition, reach-specific survival estimates were significantly higher for the injectable group in three of the eleven reaches examined. Overall, the injectable group had a 0.263 (SE = 0.017) survival probability over the entire 500 km study area compared to 0.199 (0.012) for the surgically implanted group. The differences in survival may have been caused by warm water temperatures and higher rates of infection experienced by the surgically implanted group due to the presence of sutures acting as an attachment site for pathogens. The reduction in size and ability to implant the new transmitter via injection has further reduced the tag or tagging effect bias associated with studying small fishes. As a result, the information gathered with this new technology is helping minimize the impact of dams on fish, leading to more environmentally sustainable energy systems.

13 HYDRO ENERGY↗
DOE OSTI2011

Cylindrical and spherical electron acoustic solitary waves with nonextensive hot electrons

Nonlinear propagation of cylindrical and spherical electron-acoustic solitons in an unmagnetized plasma consisting cold electron fluid, hot electrons obeying a nonextensive distribution and stationary ions, are investigated. For this purpose, the standard reductive perturbation method is employed to derive the cylindrical/spherical Korteweg-de-Vries equation, which governs the dynamics of electron-acoustic solitons. The effects of nonplanar geometry and nonextensive hot electrons on the behavior of cylindrical and spherical electron acoustic solitons are also studied by numerical simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2011

Hybrid (particle in cell-fluid) simulation of ion-acoustic soliton generation including super-thermal and trapped electrons

The present paper is devoted to the simulation of the nonlinear disintegration of a localized perturbation into an ion-acoustic soliton in a plasma. Recently, this problem was studied by a simple model [H. Abbasi et al., Plasma Phys. Controlled Fusion 50, 095007 (2008)]. The main assumptions were (i) in the electron velocity distribution function (DF), the ion-acoustic soliton velocity was neglected in comparison to the electron thermal velocity, (ii) on the ion-acoustic evolution time-scale, the electron velocity DF was assumed to be stationary, and (iii) the calculation was restricted to the small amplitude case. In order to generalize the model, one has to consider the evolution of the electron velocity DF for finite amplitudes. For this purpose, a one dimensional electrostatic hybrid code, particle in cell (PIC)-fluid, was designed. It simulates the electrons dynamics by the PIC method and the cold ions dynamics by the fluid equations. The plasma contains a population of super-thermal electrons and, therefore, a Lorentzian (kappa) velocity DF is used to model the high energy tail in the electron velocity DF. Electron trapping is included in the simulation in view of their nonlinear resonant interaction with the localized perturbation. A Gaussian initial perturbation is used to model the localized perturbation. The influence of both the trapped and the super-thermal electrons on this process is studied and compared with the previous model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2012

The effect of dust size distribution and dust charge fluctuation on dust acoustic solitary waves in a magnetized dusty plasma

Dust acoustic solitary waves in a magnetized dusty plasma are studied taking into account the effect of dust size and dust charge variations. Using the analytical and numerical results the influence of different plasma parameters on dust acoustic solitary waves are examined. It is observed that the dust size distribution and dust charge variation have significant contributions on the characteristics of dust acoustic solitary waves.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗