Updating 2D acoustic models with the constitutive relation error method

Noise-induced hearing loss has become an increasing concern for employees in the aquaculture industry. Deafness, hearing loss and hearing impairment have all been identified as some of the most common injury claims from aquaculture labourers. Despite this information, noise levels and associated noise exposures in facilities have been highly undocumented. This research aims to document information on noise exposure in aquaculture and identify short- and long-term solutions to high exposures experienced by employees. Data was collected at four aquaculture facilities in Canada. Noise sources were identified and analyzed in narrowband frequency. Noise exposures were also measured and compared with the recommendations outlined by the Canadian Standards Association. Exposures were observed to be highest during tasks within the vicinity of machinery and other mechanical equipment. Short-term solutions were identified through the selection of appropriate hearing protection. Engineering design solutions were then applied to assess the feasibility of long-term solutions to reduce exposures in facilities. Numerical acoustic simulations were performed on a facility model where the Design of Experiments methodology was applied to validate its acoustical properties. The simulations showed that design solutions could be applied to reduce noise transmission and lower exposure levels throughout the facility.

A method of structural damage identification using harmonic excitation force is presented. It considers the effects of both measurement and modelling errors in the baseline finite element model. Damage that accompanies changes in structural parameters can be estimated for a damaged structure from the change between measured vibration responses and ones calculated from the analytical model of the intact structure. In practice, modelling errors exist in the analytical model due to material and geometric uncertainties and a reduction in the degrees of freedom as well as measurement errors, making identification difficult. To surmount these problems, bootstrap hypothesis testing, which enables statistical judgment without information about these errors, was introduced. The method was validated by numerical simulation using a three-dimensional frame structure and real vibration data for a three-storey steel frame structure. Copyright © 2005 John Wiley & Sons, Ltd.

On choice and effect of weight matrix for response sensitivity-based damage identification with measurement and model errors

Domain decomposition based finite element verification in linear framework

Preface. About the Authors. Acknowledgments. Chapter 1 Introduction to Architectural Acoustics and Basic Principles (WILLIAM J. CAVANAUGH). 1.1 Introduction. 1.2 Basic Concepts. 1.3 Design Criteria. 1.4 Selected Standards in Building Acoustics. Further Reading. CASE STUDY: Fogg Art Museum Lecture Hall, Harvard University (1895 1973) 33. Chapter 2 Acoustical Materials and Methods (REIN PIRN AND JEFFREY L. FULLERTON). 2.1 Introduction. 2.2 Sound Attenuation. 2.3 Sound Absorption 44. 2.4 Common Building Materials. 2.5 Acoustical Materials. 2.6 Special Devices. 2.7 Performance Tables. References. Further Reading. Selected Web Resources. CASE STUDY: Duke University Chapel: A Lesson on Acoustical Materials. CASE STUDY: Boston Waterfront Development Under a Flight Path: Sound-Isolating Facade. Constructions to Reduce Intrusive Noise. CASE STUDY: Berklee College of Music: Sound-Isolating Constructions Between Percussion Studios. Chapter 3 Building Noise Control Applications (GREGORY C. TOCCI). 3.1 Introduction. 3.2 Acoustical Analysis. 3.3 Standards Organizations for the Building Industry. 3.4 Overview of Building Noise Control Design. 3.5 Heating, Ventilating, and Air-Conditioning System Noise Control. References. Further Reading. CASE STUDY: Mechanics Hall, Worcester, Massachusetts: Cooling Tower Sound Isolation. CASE STUDY: Field Impact Insulation Class Rating Measurements of Condominium Floor/Ceiling Construction. CASE STUDY: Noise Insulation Class Rating Measurements of a Hotel Guestroom Door. CASE STUDY: 500 Atlantic Avenue Hotel/Condominium Building Vibration Isolation. Chapter 4 Acoustical Design: Places for Listening (L. GERALD MARSHALL). 4.1 Introduction. 4.2 Sound Outdoors and Transition to Indoor Acoustics. 4.3 Concert Halls and Recital Halls. 4.4 Opera Houses, Theaters, General-Purpose Auditoriums, and Worship Spaces. 4.5 Other Places for Speech and Music Activities. References. Further Reading. CASE STUDY: Holy Cross Church, Dewitt, New York. CASE STUDY: Mitchell Hall at the University of Delaware, Newark, Delaware Historic Renovation. CASE STUDY: The New Hitchcock Presbyterian Church, Scarsdale, New York. CASE STUDY: Katherine M. Elfers Concert Hall at the Esther Eastman Music Center, Hotchkiss School, Lakeville, Connecticut. CASE STUDY: Ozawa Hall, Tanglewood Music Center, Lenox, Massachusetts. CASE STUDY: Hollywood Bowl Stage Redesign, Los Angeles, California. Chapter 5 Sound Systems (MATTHEW J. MOORE). 5.1 Introduction. 5.2 Loudspeaker Systems. 5.3 Equipment. 5.4 Examples of Sound Reinforcement and Reproduction Systems. 5.5 Special Sound System Installations. Further Reading. CASE STUDY: Hanover Theatre, Worcester, Massachusetts. CASE STUDY: Agganis Arena and Boston University Fitness and Recreation Center, Boston, Massachusetts. CASE STUDY: Ave Maria University Oratory, Ave Maria, Florida. CASE STUDY: University of Connecticut Student Union, Storrs, Connecticut. CASE STUDY: Rhode Island Senate Chamber, Providence, Rhode Island. CASE STUDY: Rhodes Arts Center, Northfield Mount Hermon School, Gill, Massachusetts. CASE STUDY: Tufts University Granoff Music Center, Medford, Massachusetts. CASE STUDY: Jay Pritzker Music Pavilion, Chicago, Illinois. Chapter 6 Recent Innovations in Acoustical Design and Research (GARY W. SIEBEIN AND BERTRAM Y. KINZEY, JR.). 6.1 Introduction. 6.2 Understanding and Measuring Room Acoustic Qualities. 6.3 Acoustical Modeling and Aural Simulation. 6.4 Other Directions in Architectural Acoustics Research. 6.5 Conclusions. References. Further Reading. CASE STUDY: Recent Halls for the Performing Arts and Acoustical Model Studies. CASE STUDY: Segerstrom Hall, Orange County Performing Arts Center, Orange County, California. CASE STUDY: McDermott Concert Hall, Morton H. Meyerson Symphony Center, Dallas, Texas. CASE STUDY: Evangeline Atwood Concert Hall, Alaska Center for the Performing Arts, Anchorage, Alaska. CASE STUDY: Bass Performance Hall, Fort Worth, Texas. CASE STUDY: The Esplanade Concert Hall, Theaters on the Bay, Singapore. CASE STUDY: Tokyo Opera City Concert Hall, Takemitsu Memorial, Tokyo, Japan. CASE STUDY: Walt Disney Concert Hall, Los Angeles, California. CASE STUDY: Computer Model Tests. Chapter 7 Sustainable Design and Acoustics (ETHAN SALTER, LEED AP). 7.1 Introduction. 7.2 Organizations Leading the Green Building Movement. 7.3 Acoustical Challenges of Green Design. 7.4 Postoccupancy Evaluations of Green Buildings. 7.5 Examples of Building Types Designed for Green Ratings. 7.6 Conclusion. References. Further Reading. Internet and Web Resources. CASE STUDY: Global Ecology Research Center at Stanford University, Palo Alto, California. CASE STUDY: Dougherty Valley High School, San Ramon, California. CASE STUDY: Charles Salter Associates Tenant Improvement Office, San Francisco, California. Appendixes. Appendix A: Conversion Factors, Abbreviations, and Unit Symbols. Appendix B: Acoustical Societies Throughout the World. Appendix C: Selection of an Acoustical Consultant. Appendix D: Self-Study Guide for Using this Book. Glossary.

The quantities of interest in geosciences (e.g., atmospheric wind, rock porosity) typically exhibit structure across a wide range of spatial and temporal scales. As a result, it is insufficient to define measurement errors as some deviations from a reference truth. The main issue is that different instruments and models filter reality in distinct ways. Without a precise mathematical description of this filtering, the exact nature of measurement, model, and representation errors remains unclear. To address this, a measurement model that specifies which geophysical field is being sampled and where is proposed. Based on this model, formal definitions for measurement, model, and representation errors are given. The distinction between these types of errors is also discussed. A novel aspect of this study is the demonstration of conditions under which measurement and representation errors will be correlated with one another. Using this new framework, two common measurement error estimation methods, namely the triple‐cornered hat and triple collocation, are shown to be special cases of a more general approach, called sampling‐aware field‐informed retrieval, that explicitly accounts for representation error. Idealized experiments are conducted to illustrate how this general approach performs in various measurement error estimation scenarios involving representation errors. In the more realistic scenarios where observations originate from differently sized sampling windows and are not perfectly collocated with one another, it is shown that estimating measurement errors is possible only if representation errors are accounted for. It is also demonstrated that displacement errors must be taken into account to avoid correlations between measurement and representation errors. More generally, this study advocates that the formulation of a measurement model is necessary for the measurement and representation errors to be clearly defined.

Numerical modeling and simulation of complex systems is continuously developing at an incredible rate in many fields of engineering and sciences. This development has been made possible thanks to the constant evolution of numerical techniques and increasing availability of computational resources. Nowadays, simulations are essential tools for engineers throughout the design process. The simulations minimize the need for costly physical experiments that may be even impossible during early design stages. However, numerical simulations have to be carefully designed, performed, and verified, to yield useful and reliable information regarding the system being studied. In fact, the confidence one has in a computation is a key aspect when interpreting and analyzing simulations results. Indeed, simulations inherently involve errors, understanding and quantification of which is critical to assess the differences between the numerical predictions and actual system behavior.

The signs and symptoms of systolic heart failure are frequently insensitive and nonspecific, making an accurate bedside diagnosis of left ventricular systolic dysfunction (LVSD) challenging. B-type natriuretic peptide (BNP) is often used, but is not diagnostically useful when in the indeterminate range. The authors investigated the diagnostic test characteristics of acoustic cardiographic parameters to identify patients with LVSD. Four hundred thirty-three patients with contemporaneous measurements of computerized acoustic cardiography, BNP, and echocardiography were included. The acoustic cardiographic model outperformed BNP alone at detecting reduced left ventricular ejection fraction (C statistic, 0.88 vs 0.67; P<.0001). The acoustic model with BNP did not perform better than the acoustic model alone (P=.14). Within the indeterminate BNP range, the acoustic model outperformed BNP (C statistic, 0.89 vs 0.64; P<.0001). Noninvasive computerized acoustic cardiography predicted LVSD in a diverse population. This acoustic cardiographic model outperformed BNP alone for predicting LVSD.

Most modern acoustic simulation programs use geometric acoustics to auralize the sound within a space, providing acceptable results in a relatively quick manner. Still, because of the limitations of geometric acoustics in computer simulations, it is of particular interest to investigate the relationship between model geometry, simulation parameters, and the listener’s perception of auralizations created from iterations of an acoustic model. In the context of this study, differences between auralizations were investigated from a perceptual standpoint, using the subjective judgment of listeners. A modified version of the Multiple Stimulus test with Hidden Reference and Anchor methodology was used to define a threshold at which an increase in model precision no longer results in perceived differences between auralizations, aiming to inform efficient modeling practices in practical applications. Acoustic simulations were performed on a set of speech and music venues modeled at incremental geometrical precision regarding their real-life counterparts, ultimately resulting in a series of auralizations that reflected changes in modeling precision. Single factor analysis of variance and a series of two-sample t-tests indicate that increasing acoustic model precision may not always result in perceptually relevant differences.

In the studies of room acoustics, the parameters that are taken into consideration while evaluating the acoustic performance of a hall are examined in two sections: objective acoustic parameters which can be measured and calculated and, subjective acoustic parameters which define the impacts of a hall's acoustical qualities over the audiences. During the first half of the century, the reverberation time was considered to be the most important objective acoustic parameter which defined the acoustic quality of a hall. But, the later studies indicated that this parameter wasn't sufficient alone to define a subjective evaluation. Consequently, studies have been made to define the subjective influence on people from the acoustic performance of a hall. In this study, it has been attempted to define the statistical correlation between the subjective evaluation of the acoustic characteristics of the four concert halls, which was done by the members of orchestra together with the ordinary concert‐goers, and their objective acoustic parameters are explained. The detailed objective acoustic parameters are presented for measurements in four concert halls in Istanbul. The halls are used by many different performances, from concerts to ballet or opera. The measured octave band quantities are RT, EDT, G,C80 and D50.

In this paper, we develop an error estimator that enables to control effectively the quality of numerical solutions obtained using proper generalised decomposition. The method is based on the Constitutive Relation Error and the construction of associated admissible fields. It takes all error sources (discretisations, truncation of the modal representation, etc.) into account and can be used, introducing adjoint-based techniques, for goal-oriented error estimation. Furthermore, specific indicators can be derived to split error contributions and thus drive adaptive procedures in an optimal manner.

One goal of numerical simulation in ocean acoustics is the accurate prediction of acoustic field properties in complex environments. In this context, the objective assessment of both sonar performance and associated signal-processing schemes operating within uncertain littoral environments pose significant challenges for numerical simulation. Some of the issues involved with using numerical models for the prediction of sonar performance in the presence of incomplete environmental information are addressed. To achieve credibility as a prediction tool, the simulation results should be held to the same standards as those associated with experimental results, i.e., the numerical predictions should include error bars or confidence intervals that represent a quantitative measure of the environmental uncertainty that is always present. Providing a rational, quantitative assessment of system uncertainty in complex environments is a nontrivial task. Incomplete environmental information occurs at multiple space-time scales within both the oceanographic and acoustic modeling phases; this uncertainty can propagate, interact, and amplify in the course of transmitting and processing the signal. It is argued that environmental uncertainty should be directly incorporated into the numerical simulation process so that it links directly to the physics of wave propagation. [Work supported by NRL Base funding through ONR.]

Transcranial focused ultrasound has emerged as a promising and innovative neuromodulation technique with the potential to foster brain disease therapies. Its efficacy depends on the precise targeting of specific brain regions using ultrasound waves. Achieving this precision requires the careful design of devices, with numerical simulations playing a critical role. Specifically, accurate modeling of the head structure is essential for optimizing the performance of the focused ultrasound transducer. This study investigates the impact of various skull models on the pressure field generated by two geometrically distinct focused ultrasound transducers. By quantifying the focal and scattered volumes beyond the skull, we gain valuable insights into the performance of each transducer. Furthermore, theanalysis emphasizes the importance of using a realistic porous skull model over a non-porous one inacoustic numerical simulations. The main goal of this work is to investigate numerical simulations outcomes using accurate skull models for testing numerically designed focused ultrasound transducers. Numerical simulations were performed using one homogeneous skull model (0% porosity) and two heterogeneous models with varying porosity levels (50% and 60%), that reflect the current understanding of human skull structure. The output pressure was generated by two distinct multi-element transducers (f-44 number = 0.8 and f-number = 1.1), each consisting of 96 active elements, positioned at a fixed distance above the skull models across all simulation scenarios. Acoustic numerical simulations were conducted using the MATLAB toolbox k-Wave and applying the time-reversal technique to correct for phase aberrations induced by the skull. The output data comprises 3D maps of pressure distribution. For each simulation, the Full Width at Half Maximum (FWHM) was calculated to obtain the -6 dB pressure isosurfaces, which were then used to determine the actual focal volume and the scattered pressure volume. Additionally, the focus size and its deviation from the target zone were analyzed to quantify the impact of the trabecular structure on ultrasound targeting. The numerical simulations revealed differences in the sonication performance of the two transducers. The non-porous model (0% porosity) exhibited almost no scattered volume compared to porous models (50% and 60% porosity) for both transducers. However, simulations using different porosity percentages did not show a consistent trend in variations of focus shift or FWHM. Neglecting the trabecular structure in transcranial numerical simulations results in inaccurate predictions of pressure distribution, potentially leading to unintended sonication of brain regions beyond the intended target. Simulating with homogeneous models can mask the occurrence of pressure hotspots and significant scattering beyond the skull, thereby creating a false impression of enhanced transducer performance.While the trabecular structure may not influence focus shift and size, it reveals a distinctly differentpressure distribution compared to homogeneous models. Consequently, employing heterogeneous models is crucial for accurately testing and predicting the pressure field generated by an ultrasound transducer. This approach ensures greater accuracy, precision, and selectivity, particularly when optimizing the transducer's configuration during the design and testing phases.

Accurate sound rendering can add significant realism to complement visual display in interactive applications, as well as facilitate acoustic predictions for many engineering applications, like accurate acoustic analysis for architectural design. Numerical simulation can provide this realism most naturally by modeling the underlying physics of wave propagation. However, wave simulation has traditionally posed a tough computational challenge. In this paper, we present a technique which relies on an adaptive rectangular decomposition of 3D scenes to enable efficient and accurate simulation of sound propagation in complex virtual environments. It exploits the known analytical solution of the Wave Equation in rectangular domains, and utilizes an efficient implementation of the Discrete Cosine Transform on Graphics Processors (GPU) to achieve at least a 100-fold performance gain compared to a standard Finite-Difference Time-Domain (FDTD) implementation with comparable accuracy, while also being 10-fold more memory efficient. Consequently, we are able to perform accurate numerical acoustic simulation on large, complex scenes in the kilohertz range. To the best of our knowledge, it was not previously possible to perform such simulations on a desktop computer. Our work thus enables acoustic analysis on large scenes and auditory display for complex virtual environments on commodity hardware.

Uncertainty quantification of creep in concrete by Taylor expansion