Computational Acoustics Research Articles (Page 1)

Accurately reconstructing the acoustics of lost historical spaces requires interdisciplinary approaches and careful consideration of uncertainty. This study presents the development of an acoustical model of a Romanesque cathedral, termed Notre-Dame II, that was replaced in the 12th century by the current cathedral in Paris. This was done to examine the acoustic conditions under which early Parisian polyphonic music may have been performed. Notre-Dame II, described in surviving documentation and found in ruins underneath the modern cathedral, required reconstruction through indirect evidence due to the absence of detailed architectural plans or descriptions. A proxy church of similar era and construction was selected to approximate key geometrical and material parameters. Material properties were estimated based on comparable historical structures, and the calibration relied on known acoustic metrics in the proxy, including reverberation time and clarity. Uncertainty arose at multiple stages, including limited documentation of Notre-Dame II, assumptions about proxy church materials, and the inherent limitations of computational acoustics methods. This study highlights how iterative calibration and sensitivity analyses mitigate these challenges, providing insights into the use of extant structures to study the acoustic experience of lost buildings.

We present an efficient method for computing acoustic energy within complex-shaped geometries and cavities with obstacles in the mid-high frequency range. This method is based on the Simplified Energy Method (MES), known for its accuracy in such frequency ranges but traditionally limited to simple cavity shapes. The proposed hybrid method integrates the techniques of ray and triangle intersection with the MES formulation to address these limitations. By calculating the intersection points of the rays, we identify the obstructing elements before computing the energy transfer between the boundary elements. A primary intersection state matrix, containing direct view information between elements, is integrated into a modified MES equation to eliminate unnecessary computations and ensure precise calculations of energy transfer for obstructed elements. This hybrid approach is applied to both direct and reverberant fields to determine the total energy density. Numerical simulations conducted within a complex domain enclosure demonstrate the precision of the proposed algorithms when compared to traditional MES calculations. In addition, we propose applying this method to robust shape optimization, effectively balancing competing criteria to achieve optimized acoustic performance. This refined and computationally feasible tool significantly advances computational acoustics, providing accurate and efficient design solutions for complex environments.

A two-dimension hybrid computational acoustics method to predict the far-field noises

Many realistic problems in computational acoustics involve complex geometries and sound propagation over large domains, which requires accurate and efficient numerical schemes. It is difficult to meet these requirements with a single numerical method. Pseudo-spectral (PS) methods are very efficient, but are limited to rectangular shaped domains. In contrast, the nodal discontinuous Galerkin (DG) method can be easily applied to complex geometries, but can become expensive for large problems. In this paper, we study a coupling strategy between the PS and DG methods to efficiently solve time-domain acoustic wave problems. The idea is to combine the strengths of these two methods: the PS method is used on the part of the domain without geometric constraints, while the DG method is used around the PS region to accurately represent the geometry. This combination allows for the rapid and accurate simulations of large-scale acoustic problems with complex geometries, but the coupling and the parameter selection require great care. The coupling is achieved by introducing an overlap between the PS and DG regions. The solutions are interpolated on the overlaps, which allows the use of unstructured finite element meshes. A standard explicit Runge–Kutta time-stepping scheme is used with the DG scheme, while implicit schemes can be used with the PS scheme due to the peculiar structure of this scheme. We present one- and two-dimensional results to validate the coupling technique. To guide future implementations of this method, we extensively study the influence of different numerical parameters on the accuracy of the schemes and the coupling strategy.

Abstract To optimize the control of twin-screw pumps (TSP), it is essential to ensure performance while simultaneously preventing noise levels from exceeding specified limits. In order to investigate the flow characteristics and the mechanism of flow-induced radiated noise under various operating conditions, a coupled numerical simulation approach using Computational Fluid Dynamics (CFD) and Computational Acoustics (CA) was employed, and the findings were validated through experiments. The study reveals that the highest sound pressure levels of radiated noise occur near the rotor outlet surface, and the dynamic-static interference at the rotor-boundary interface is the primary cause of flow-induced noise in TSP. The flow-induced noise in TSP exhibits dipole characteristics, with the overall sound pressure level gradually increasing and then sharply rising with the increase in rotational speed (or flow rate). It stabilizes near the design operating point and continues to increase steadily with further increases in rotational speed (or flow rate). Modal testing using a dual-distributed measurement technique highlights significant interference effects between the rotor inlet and the dynamic rotor for the first two Blade Passing Frequencies (BPF) in TSP. The results of this study provide a theoretical foundation for the low-vibration and low-noise design and operation of TSP.

Low Mach number approximated linearized perturbed Euler equations-based unified computational flow-induced acoustic model for 2D planar/axisymmetric complex geometry

The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40 + faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. Distance education students pursuing the M.Eng. degree join resident students in a hybrid classroom environment. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends.

The Silver Medal is presented to individuals, without age limitation, for contributions to the advancement of science, engineering, or human welfare through the application of acoustic principles, or through research accomplishment in acoustics.

Computational acoustics is in its second full year as a Technical Committee, after being promoted from a Technical Specialty Group in May 2022. The CA TC provides a forum for researchers interested in numerical methods pertinent to acoustic wave propagation and structures, data analytics, validation, optimization, visualization, as well as application of computational models to engineering, noise control, and other practical problems. Some areas of current and emerging interest include artificial intelligence, uncertainty characterization, model reduction techniques, efficient parallelization, and time-domain treatments of dissipation. The CA TC is also interested in building collaborations with other TCs on repositories and benchmarks for codes and data.

Musical acoustics was launched as one of the first Technical Committees of the Acoustical Society of America. The Technical Committee in Musical Acoustics (TCMU) is concerned with applying science and technology to the field of music. The four main areas are (1) physics of musical sound production in musical instruments and the voice, (2) music perception and cognition, (3) analysis and synthesis of musical sounds and compositions, and (4) recording and reproduction technology. The scopes of areas have changed over time; for example, current interests in using groundbreaking methods in artificial intelligence and computational acoustics to solve problems. There is substantial interdisciplinary overlap with other technical committees, such as Architectural Acoustics and Physiological and Psychological Acoustics. Musical acoustic studies sometimes only require relatively moderate equipment. Thus, they lend themselves well as a research entry point for undergraduate and even high school students—especially since there is often a natural interest in music from early on. However, research in the field can also become very complex and often requires cultural understanding and listening skills to interpret technical results and direct research meaningfully. On the practical side, the TCMU sometimes organizes concerts to augment the technical sessions.

This study presents a comprehensive investigation of the internal noise characteristics of a mixed-flow pump by combining computational fluid dynamics (CFD) and computational acoustics. The turbulent flow field of the pump is simulated using the unsteady SST k-ω turbulence model in CFD. The contributions of the volute, guide vanes, and impeller to the internal noise are analyzed and compared using the Lighthill theory, FW-H formula, and LMS Virtual Lab software for acoustic simulation. The research findings indicate that the energy of pressure fluctuations in the mixed-flow pump is predominantly concentrated at the blade passing frequency and its low-frequency harmonics. This suggests that the internal noise is mainly in the low-frequency range, with higher energy at the blade passing frequency and its harmonics. Under the 0.6Qdes flow condition, the flow inside the pump becomes more complex, resulting in higher sound pressure levels and sound power levels compared to higher flow conditions. However, for flow conditions ranging from 0.8Qdes to 1.2Qdes, the sound pressure levels gradually increase with increasing flow rate, with the sound pressure level at 1.0Qdes being nearly identical to that at 1.2Qdes. The analysis of sound power level spectra at different flow rates reveals that the distribution characteristics of internal vortex structures directly impact the hydrodynamic noise inside the mixed-flow pump. These research findings provide a significant theoretical basis for noise control in mixed-flow pumps.

Discretization-based methods like the finite element method have proven to be effective for solving the Helmholtz equation in computational acoustics. However, it is very challenging to incorporate measured data into the model or infer model input parameters based on observed response data. Machine learning approaches have shown promising potential in data-driven modeling. In practical applications, purely supervised approaches suffer from poor generalization and physical interpretability. Physics-informed neural networks (PINNs) incorporate prior knowledge of the underlying partial differential equation by including the residual into the loss function of an artificial neural network. Training the neural network minimizes the residual of both the differential equation and the boundary conditions and learns a solution that satisfies the corresponding boundary value problem. In this contribution, PINNs are applied to solve the Helmholtz equation within a two-dimensional acoustic duct and mixed boundary conditions are considered. The results show that PINNs are able to solve the Helmholtz equation very accurately and provide a promising data-driven method for physics-based surrogate modeling.

The boundary element method (BEM) is a powerful algorithm to solve the Helmholtz equation for harmonic acoustic waves. The explicit use of Green’s functions avoids domain truncation of unbounded regions and accurately models wave propagation through homogeneous materials. Furthermore, fast multipole and hierarchical compression techniques provide efficient computations for dense matrix multiplications. However, the convergence of the iterative linear solvers deteriorates significantly when frequencies are high or materials have large contrasts in density or speed of sound. This talk presents several algorithmic improvements of the BEM. First, a preconditioner based on on-surface radiation conditions drastically reduces the iteration count of linear solvers at high frequencies. Second, anovel boundary integral formulation remains well-conditioned for high-contrast transmission problems. We used our fast and accurate BEM implementation to simulate focused ultrasound propagation in the human body, which can be translated to important biomedical applications such as the non-invasive treatment of liver cancer and neuromodulation of the brain. We validated the methodology within the benchmarking exercise of the International Transcranial Ultrasonic Stimulation Safety and Standards (ITRUSST) consortium. As a second application, we simulated the collective resonances of water-entrained arrays of air bubbles. Finally, we implemented all functionality in our open-source Python library, OptimUS.

The structure of a muzzle brake has a significant effect on the overall technical performance of an automatic weapon. This paper presents a multi-objective optimization of a muzzle brake that enhances the overall performance, that is, it increases the muzzle brake efficiency and simultaneously decreases the noise emanating from the rifle upon discharge. A standard impact-reaction muzzle brake was selected as the research object. The optimal values of four structural parameters were established through multi-objective optimization. The process consists of design of experiments, coupled computational fluid dynamics and computational acoustics calculations, an approximation model, and a multi-objective genetic algorithm. Based on the results, the correlation and sensitivity between the structural parameters and the objectives were investigated. Moreover, the reaction force and noise directivity of the optimized muzzle brake were compared with those of the original design. The results show that the disk angle of the side holes was the most sensitive design variable for both efficiency and noise. The optimized muzzle brake had a remarkable improvement in brake efficiency accompanied by only a small increase in the sound pressure level, so it showed better overall performance. The optimization method proposed in this paper is practical and effective for engineering design.

The accuracy of the conventional finite element (FE) approximation for the analysis of acoustic propagation is always characterized by an intractable numerical dispersion error. With the aim of enhancing the performance of the FE approximation for acoustics, a coupled FE-Meshfree numerical method based on triangular elements is proposed in this work. In the proposed new triangular element, the required local numerical approximation is built using point interpolation mesh-free techniques with polynomial-radial basis functions, and the original linear shape functions from the classical FE approximation are employed to satisfy the condition of partition of unity. Consequently, this coupled FE-Meshfree numerical method possesses simultaneously the strengths of the conventional FE approximation and the meshfree numerical techniques. From a number of representative numerical experiments of acoustic propagation, it is shown that in acoustic analysis, better numerical performance can be achieved by suppressing the numerical dispersion error by the proposed FE-Meshfree approximation in comparison with the FE approximation. More importantly, it also shows better numerical features in terms of convergence rate and computational efficiency than the original FE approach; hence, it is a very good alternative numerical approach to the existing methods in computational acoustics fields.

This work examines the use of generative adversarial networks for reconstructing sound fields from experimental data. It is investigated whether generative models, which learn the underlying statistics of a given signal or process, can improve the spatio-temporal reconstruction of a sound field by extending its bandwidth. The problem is significant as acoustic array processing is naturally band limited by the spatial sampling of the sound field (due to the difficulty to satisfy the Nyquist criterion in space domain at high frequencies). In this study, the reconstruction of spatial room impulse responses in a conventional room is tested based on three different generative adversarial models. The results indicate that the models can improve the reconstruction, mostly by recovering some of the sound field energy that would otherwise be lost at high frequencies. There is an encouraging outlook in the use of statistical learning models to overcome the bandwidth limitations of acoustic sensor arrays. The approach can be of interest in other areas, such as computational acoustics, to alleviate the classical computational burden at high frequencies.

Investigation on the flow-induced structure noise of a submerged cone-cylinder-hemisphere combined shell

<sec>Contrast-enhanced ultrasound imaging (CEUS) based on the acoustic nonlinearity of ultrasonic microbubble has received great attention in recent years. Compared with conventional linear ultrasound imaging, nonlinear CEUS can further improve the imaging resolution while overcoming the challenge of clutter filtering. Simulation, acting as an effective tool for research on new mechanisms and technologies of ultrasound imaging, has been a long-term focus of computational acoustics. In the community of biomedical ultrasound, common sound field simulation tools are mainly based on finite element method (FEM), analytical method, <i>k</i>-space pseudospectral method and finite-difference time-domain method (FDTD), which are relatively mature solutions for simulating the nonlinear characteristics of tissue. However, it is still not trivial to simulate nonlinear CEUS by using the prevailing methods, as the nonlinearity of microbubble is often not considered.</sec><sec>In this paper, we propose a simulation method of nonlinear CEUS imaging that successfully combines the microbubble nonlinearity and classic <i>k</i>-space pseudospectral method. Specifically, forced oscillation response of the microbubble is computed based on the modified Rayleigh-Plesset equation and such a nonlinear response is further dealt as an additional source for analyzing the nonlinear component propagation and CEUS imaging. To investigate the performance of the proposed method, B-mode images of single microbubble and clustered microbubbles are simulated based on plane wave imaging. The plane wave based CEUS imaging can thus be carried out with different compounding angles and different contrast pulse sequencing (CPS) strategies (pulse inversion, amplitude modulation, pulse inversion & amplitude modulation, and probe element alternation). Different soft-tissue and mechanical parameters of the microbubble can be adjusted by using the proposed nonlinear simulation strategy, thus providing efficient solution for CEUS simulation. Such a method can evaluate the performances of different CPS strategies, and further contribute to the CEUS development.</sec>

To study tissue permeability under extracorporeal exposure to various types of radiation, it is necessary to use multiphysical and mathematical modeling of all stages of the technology being developed, taking into account the dielectric and acoustic properties of human body tissues. The technologies used in medicine with focused ultrasound often have a high level of artifacts in the visualization of diagnostic studies and complications during therapy. When modeling exposure to focused ultrasound, the applied solvers must take into account pressure wave propagation, density changes and jumps, non-linearity and diffusion losses that occur in human tissues, and offer simulation of acoustic propagation in inhomogeneous installations in near real time. The article presents the results of multiphysics and mathematical modeling in the Sim4Life for Science V7.0 package using a full-wave acoustic solver (P-ACOUSTICS) of modern computational acoustics.

The Graduate Program in Acoustics at Penn State offers graduate degrees (M.Eng., M.S., Ph.D.) in Acoustics, with courses and research opportunities in a wide variety of subfields. Our 820 alumni are employed around the world in a wide variety of military and government labs, academic institutions, consulting firms, and consumer audio and related industries. Our 40+ faculty from several disciplines conduct research and teach courses in structural acoustics, nonlinear acoustics, architectural acoustics, signal processing, aeroacoustics, biomedical ultrasound, transducers, computational acoustics, noise and vibration control, acoustic metamaterials, psychoacoustics, and underwater acoustics. Course offerings include fundamentals of acoustics and vibration, electroacoustic transducers, signal processing, acoustics in fluid media, sound and structure interaction, digital signal processing, experimental techniques, acoustic measurements and data analysis, ocean acoustics, architectural acoustics, noise control engineering, nonlinear acoustics, outdoor sound propagation, computational acoustics, biomedical ultrasound, flow induced noise, spatial sound and three-dimensional audio, and the acoustics of musical instruments. This poster highlights faculty research areas, laboratory facilities, student demographics, successful graduates, and recent enrollment and employment trends for the Graduate Program in Acoustics at Penn State.