Acoustic sensitivity analysis considering ground reflection using Bayesian neural network-accelerated isogeometric boundary element method

Acoustic sensitivity analysis is an essential technique to determine the direction of structural-acoustic optimization by evaluating the gradient of the objective functions with respect to the design variables. However, acoustic sensitivity analysis with respect to acoustic impedance, which is an important parameter representing the interior absorbent material in automotive acoustics, is lacking in the study. Moreover, acoustic sensitivity analysis implemented with conventional numerical methods is time and effort-consuming in automotive acoustics, due to the large-scale mesh generation. In this work, the impedance sensitivity analysis for automotive acoustics based on the discontinuous isogeometric boundary element method is presented. The regularized boundary integral equation with impedance boundary conditions is established, then the sensitivity is derived by differentiating the boundary integral equation. The efficiency of the proposed method is improved by employing the parallel technique and generalized minimal residual solver. A long duct example with an analytical solution validates the accuracy of the proposed method, and an automotive passenger compartment subjecting to impedance boundary conditions illustrates that the computing time of the proposed method is one order of magnitude less than the conventional method. This work presents an easily implementable and efficient tool to investigate acoustic sensitivity with respect to impedance, showing great potential in the application of automotive acoustics.

The determination of the acoustic sensitivity characteristics with respect to the variation of the design parameters can provide a method for low-noise design of mechanical structure objectively and quantitatively.Based on the boundary element method, the expressions of sensitivities of the acoustic pressure with respect to changes in frequency and surface vibration velocity are presented.The sensitivities of coefficient matrix are computed by differentiation directly,instead of finite difference approximation. Using displaced points method,the singular integral can be obtained fast and correctly.Numerical sensitivity results obtained by the boundary element method are compared to analytical solutions,and the validation and availability of the present method are proved. These acoustic sensitivity analysis methods can lay a solid foundation for the low-noise design of mechanical structure.

An isogeometric approach of two dimensional acoustic design sensitivity analysis and topology optimization analysis for absorbing material distribution

A study on the applications of the acoustic design sensitivity analysis of vibrating bodies

In this paper, a generalized th-order perturbation method based on the isogeometric boundary element method is proposed for the uncertainty analysis of broadband structural acoustic scattering problems. The Burton-Miller method is employed to solve the problem of non-unique solutions that may be encountered in the external acoustic field, and the th-order discretization formulation of the boundary integral equation is derived. In addition, the computation of loop subdivision surfaces and the subdivision rules are introduced. In order to confirm the effectiveness of the algorithm, the computed results are contrasted and analyzed with the results under Monte Carlo simulations (MCs) through several numerical examples.

Bayesian methods have been proposed as a way of synthesizing all available evidence to inform decision making. However, few practical applications of the use of Bayesian methods for combining patient-level data (i.e., trial) with additional evidence (e.g., literature) exist in the cost-effectiveness literature. The objective of this study was to compare a Bayesian cost-effectiveness analysis using informative priors to a standard non-Bayesian nonparametric method to assess the impact of incorporating additional information into a cost-effectiveness analysis. Patient-level data from a previously published nonrandomized study were analyzed using traditional nonparametric bootstrap techniques and bivariate normal Bayesian models with vague and informative priors. Two different types of informative priors were considered to reflect different valuations of the additional evidence relative to the patient-level data (i.e., "face value" and "skeptical"). The impact of using different distributions and valuations was assessed in a sensitivity analysis. Models were compared in terms of incremental net monetary benefit (INMB) and cost-effectiveness acceptability frontiers (CEAFs). The bootstrapping and Bayesian analyses using vague priors provided similar results. The most pronounced impact of incorporating the informative priors was the increase in estimated life years in the control arm relative to what was observed in the patient-level data alone. Consequently, the incremental difference in life years originally observed in the patient-level data was reduced, and the INMB and CEAF changed accordingly. The results of this study demonstrate the potential impact and importance of incorporating additional information into an analysis of patient-level data, suggesting this could alter decisions as to whether a treatment should be adopted and whether more information should be acquired.

A method is proposed for the analysis of acoustic scenes. The contribution of each competing source is suppressed on the basis of harmonic structure or cross-sensor correlation, in such a way that other sources may be estimated. Successive suppression of sources allows the scene to be characterized. In the limit of purely periodic sources and no noise, the method provides accurate estimates of fundamental frequency and spectral content. In the presence of noise or imperfect periodicity, it offers likelihood functions from which the spectral content of sources may be inferred using Bayesian methods. The method is an alternative to more familiar spectral and spectro-temporal methods of acoustic scene analysis. An advantage is that precise analysis is possible using short temporal windows.

The “five-parameter model” is a performance model for photovoltaic solar cells that predicts the voltage and current output by representing the cells as an equivalent electrical circuit with radiation and temperature-dependent components. An important feature of the five-parameter model is that its parameters can be determined using data commonly provided by module manufacturers on their published datasheets. This paper documents the predictive capability of the five-parameter model and proposes modifications to improve its performance using approximately 30 days of field-measured meteorological and module data from a wide range of cell technologies, including monocrystalline, polycrystalline, amorphous silicon, and copper indium diselenide (CIS). The standard five-parameter model is capable of predicting the performance of monocrystalline and polycrystalline silicon modules within approximately 6% RMS but is slightly less accurate for a thin-film CIS and an amorphous silicon array. Errors for the amorphous technology are reduced to approximately 5% RMS by using input data obtained after the module underwent an initial degradation in output due to aging. The robustness and possible improvements to the five-parameter model were also evaluated. A sensitivity analysis of the five-parameter model shows that all model inputs that are difficult to determine and not provided by manufacturer datasheets such as the glazing material properties, the semiconductor band gap energy, and the ground reflectance may be represented by approximate values independent of the PV technology. Modifications to the five-parameter model tested during this research did not appreciably improve the overall model performance. Additional dependence introduced by a seven-parameter model had a less than 1% RMS effect on maximum power predictions for the amorphous technology and increased the modeling errors for this array 4% RMS at open-circuit conditions. Adding a current sink to the equivalent circuit to better model recombination currents had little effect on the model behavior.

A Shape Optimisation with the Isogeometric Boundary Element Method and Adjoint Variable Method for the Three-Dimensional Helmholtz Equation

Currently, there remains a lack of systematic quantitative analysis of the acoustic impact mechanism of ancestral temple theatres in relation to their core function of opera performance. This paper takes the Zhaomutang—a typical ancestral temple theatre in northeast Jiangxi—as an example, and comprehensively uses on-site mapping, impulse response testing, and ODEON three-dimensional sound field simulation to conduct acoustic sensitivity analysis on five key spatial elements of the theatre. The results show that the theatre has a hierarchical sound field pattern along its depth, characterized by “high in the front, low in the rear, stronger on the sides and weaker in the middle”. The front patio and the Xiangtang support the clarity of Gan opera dialogue and the fullness of singing through early lateral reflections and moderate reverberation (EDT of 0.8–1.1 s, C80 of 3.2–6.1 dB). However, the rear patio and the Qintang show apparent loudness deficiency (G of −1.5–3.2 dB) and lack of spatial immersion (LF80 below 0.23). The most effective optimization comes from the reconstruction of the geometric relationship between performers and audience: moving the performers forward and appropriately raising the stage and audience area floor can significantly shorten the rear area EDT and increase C80 and G; in contrast, the improvement in sound quality brought about by adding a patio cover and raising the gables is minimal, and the changes in various parameters are generally less than 1 JND. Based on this, the “schedule priority—reversible intervention” acoustic maintenance strategy for living heritage is proposed, and it is suggested that reversible reflective components be set in the side corridor to specifically enhance the sense of immersion in the rear area sound field. The study constructs a quantitative correlation framework of space, materials, and sound field, providing methodological support and parameter basis for the acoustic assessment and protective utilization of ancestral temple theatres.

To describe the priors and decision thresholds in phase 2 and 3 randomized controlled trials (RCTs) evaluating drug efficacy using Bayesian methods. A systematic review of phase 2 and 3 RCTs evaluating drug efficacy through Bayesian inference was conducted across the MEDLINE, EMBASE, and Cochrane databases, with no date restrictions until September 2022. The type of prior used for the analysis of the primary endpoint and its characteristics (type and parameters of the distribution, justification, and sensitivity analysis), the use of a posterior probability decision threshold defined a priori, and its value, were extracted. From 1161 articles screened, 69 articles were ultimately included, encompassing a total of 91 comparisons, as some trials assessed multiple primary endpoints or treatments. The prior was assigned to treatment effect in 51% of the cases (n=46) to each arm in 37% (n=34) and was not explicitly defined in 12% (n=11). Prior distribution was described (with its parameters) in 59% of cases (n=54). A decision threshold was set a priori in 68% of the results (n=62), and its value ranged from 70% to 99% (median 95%). The inconsistent description of priors, along with the wide variation and occasional absence of decision thresholds, underscore the need for clear guidelines on the use and reporting of Bayesian methods. Bayesian methods are being used more frequently in clinical trials to assess drug's efficacy. These methods offer flexibility by incorporating prior knowledge into the analysis. However, the use of Bayesian approaches is still not widespread, and there are challenges with how results are interpreted, partly due to a lack of clear standards. We conducted a systematic review to describe how Bayesian methods were used and reported in phase 2 and 3 clinical trials assessing drug efficacy. We looked at the types of prior information used in the analyses and how decisions about efficacy were made based on the results. Out of 1161 studies reviewed, 69 were included in the analysis, covering 91 drug comparisons. The priors in Bayesian drug trials were assigned to treatment effects or each arm, often justified but variably describe. Similarly, decision thresholds for determining drug efficacy were preset in most studies, but with heterogeneity in the thresholds used to conclude. Our findings highlight the need for clearer guidelines on using Bayesian methods in clinical trials to improve transparency and consistency in how results are reported.

We consider situations in Bayesian analysis where we have a family of priors $\nu_h$ on the parameter $\theta$, where $h$ varies continuously over a space $\mathcal{H}$, and we deal with two related problems. The first involves sensitivity analysis and is stated as follows. Suppose we fix a function $f$ of $\theta$. How do we efficiently estimate the posterior expectation of $f(\theta)$ simultaneously for all $h$ in $\mathcal{H}$? The second problem is how do we identify subsets of $\mathcal{H}$ which give rise to reasonable choices of $\nu_h$? We assume that we are able to generate Markov chain samples from the posterior for a finite number of the priors, and we develop a methodology, based on a combination of importance sampling and the use of control variates, for dealing with these two problems. The methodology applies very generally, and we show how it applies in particular to a commonly used model for variable selection in Bayesian linear regression, and give an illustration on the US crime data of Vandaele.

Shape optimization by conventional and extended isogeometric boundary element method with PSO for two-dimensional Helmholtz acoustic problems

Isogeometric boundary element method for isotropic damage elastic mechanical problems

In this paper, an optimum rib layout design method for reducing radiated noise is proposed based on topology optimization and acoustic contribution analysis. According to radiated noise depends on acoustic transfer vector (ATV) and normal velocity, the influence of rib layout on ATV is analyzed and it is found rib layout has little influence on ATV. Only if a region has maximum acoustic contribution, the normal velocities on this region can have the most remarkable influence on radiated noise. So the determination procedure of region with maximum acoustic contribution is introduced. Based on this, the topology optimization model is established to minimize the normal velocities on this region. Ribs can be arranged according to the optimum topologies to reduce the normal velocities, which in turn results in a reduction of radiated noise. The topology optimization model is used to obtain the optimal rib layout by taking a plate-like structure as an example. The plate is fixed along all side edges and excited by a time-harmonic external point load with different prescribed frequencies. The radiated noise is simulated using the finite element method and boundary element method. Four plates are manufactured according to the optimal rib layouts for different single frequency excitation. Modal test and sound measurement are conducted to validate the proposed method. The influence of loading position on the topology optimization results is also investigated and discussed.