Parametric Optimization of Local Resonant Sonic Crystals Window on Noise Attenuation by Using Taguchi Method and ANOVA Analysis

Abstract

Local resonant sonic crystals (LRSCs) window as a novel design has recently been proposed to achieve a good balance between noise mitigation, natural ventilation and natural lighting. In an effort to explore the feasibilities of such designs in civil residential buildings, an optimization methodology was proposed to develop a more compact LRSCs window with high noise attenuation performance in the present study. Specifically, the Taguchi method was adopted for the design of experiments on the parameters of interest and their corresponding levels, and SN ratio analysis was then applied for the parametric evaluations on the noise attenuation on specified frequencies in traffic noise (concentrated sound energy frequency range: 630–1000 Hz). Three optimal sets of design parameters on the interested frequencies, namely, 630 Hz, 800 Hz and 1000 Hz were obtained. ANOVA analysis was conducted to quantificationally identify the design parameters with statistical significance and remarkable contribution to the desired performance. Results indicate that the slit size has the most significant influence on the overall noise attenuation performance, followed by cavity width. An optimal set of design parameters to achieve the overall best noise reduction performance in the frequency range of 630–1000 Hz was finally determined by combining the SN ratio and ANOVA analysis. A prototype of the final optimized LRSCs window was then fabricated and tested in a semi-anechoic chamber. Good agreement was found between the experiment and numerical simulation. In comparison to the benchmark case, the final optimized design can achieve a further noise reduction by 2.84 dBA, 3.48 dBA and 5.56 dBA for the frequencies of 630 Hz, 800 Hz and 1000 Hz, respectively. The overall noise reduction for the interested frequency range can be promoted by 3.28 dBA. The results indicate that the proposed optimization methodology is practical and efficient in designing a high-performance LRSCs window or improving similar applications.

Development of Adjustable Parallel Helmholtz Acoustic Metamaterial for Broad Low-Frequency Sound Absorption Band

“…In order to obtain the desired sound absorption performance, it is essential to adjust structural parameters of the acoustic metamaterial [ 15 , 16 , 17 , 18 , 19 ]. Li et al [ 15 ] had proposed the composite structure composed of porous-material layer mosaicked with a perforated resonator and its structural parameters were optimized to enhance the low-frequency sound absorption of the porous layer.…”

“…Li et al [ 15 ] had proposed the composite structure composed of porous-material layer mosaicked with a perforated resonator and its structural parameters were optimized to enhance the low-frequency sound absorption of the porous layer. An optimization methodology was proposed by Lee et al [ 16 ] to develop a more compact local resonant sonic crystals window, and three optimal sets of design parameters on the corresponding interested frequencies of 630 Hz, 800 Hz and 1000 Hz were obtained. Ciaburro et al [ 17 ] proposed a novel layered membrane metamaterial based on three layers of the reused PVC membranes and a reused metal washer, and different configurations were analyzed by changing the number of masses attached to each layer and the geometry of their position.…”

Visually Enhanced Parallel Coordinates Plot With Two-Dimensional Kernel Density Scatter Plots

“…Analysis of variance (ANOVA) is a useful technique for determining whether and to what extent each element has an impact on the given outcomes [42][43] [44][45] [46]. Using the social science statistical online tool SPSSPRO (https://www.spsspro.com/), we first conducted a two-way analysis of variance (two-way ANOVA) of the response time of each participant in the four phases of the visual analysis assignment using three visual analysis methods.…”

Data-driven approach for prediction of band diagram of locally resonant sonic crystal

“…Moreover, the design parameter is directly related to spectral response, hence the inverse design [37][38][39][40][41][42][43] with machine learning makes it possible to determine the design parameter. Notably, most of the optimization [44,45] studies are related to structural parameters of the unit cell.…”