Johnson-Champoux-Allard Research Articles

ACOUSTIC-MODELING OF RANDOM FIBROUS MATERIALS

We present a microstructure-based model for determining the sound absorption behavior and transport parameters of random fibrous materials and exploring the physical mechanisms underlying acoustic energy dissipation. In order to increase the generalizability of the model, a three-dimensional random fiber structure is employed for simulation. The propagation of sound waves is associated with four transport parameters, including viscous permeability, tortuosity, as well as viscous and thermal characteristic lengths. These parameters are determined by the porosity and diameter of the fibrous material. By using the method of multi-scale asymptotic simulation, the theoretical model for transport parameters includes unknown coefficients that are adjusted based on the simulated results. The sound absorption coefficients are then obtained by integrating the transport parameters into the widely-used Johnson-Champoux-Allard (JCA) model for porous materials. The theoretical predictions match well with existing experimental measurements on sintered fiber metals and fibrous copper wires. Our model systematically examines the impact of fiber diameter, porosity, and material thickness on sound absorption performance. Optimal results are achieved by carefully selecting fiber diameter and porosity to enhance the acoustic dissipation of sound waves, while thicker fibrous materials increase sound absorption in the low frequency range. The model provides a theoretical framework for designing and fabricating fibrous materials to reduce noise.

Synthesis of Highly Porous Graphene Oxide-PEI Foams for Enhanced Sound Absorption in High-Frequency Regime.

High-frequency noise exceeding 1 kHz has emerged as a pressing public health issue in industrial and occupational settings. In response to this challenge, the present study explores the development of a graphene oxide-polyethyleneimine (GO-PEI) foam (GPF) featuring a hierarchically porous structure. The synthesis and optimization of GPF were carried out using a range of analytical techniques, including Raman spectroscopy, scanning electron microscopy (SEM), Braunauer-Emmett-Teller (BET) analysis, X-ray diffraction (XRD), and Fourier-transform infrared spectroscopy (FT-IR). To evaluate its acoustic properties, GPF was subjected to sound absorption tests over the 1000-6400 Hz frequency range, where it was benchmarked against conventional melamine foam. The findings demonstrated that GPF with a GO-to-PEI composition ratio of 1:3 exhibited enhanced sound absorption performance, with improvements ranging from 15.0% to 118%, and achieved a peak absorption coefficient of 0.97. Additionally, we applied the Johnson-Champoux-Allard (JCA) model to further characterize the foam's acoustic behavior, capturing key parameters such as porosity, flow resistivity, and viscous/thermal losses. The JCA model exhibited a superior fit to the experimental data compared to traditional models, providing a more accurate prediction of the foam's complex microstructure and sound absorption properties. These findings underscore GPF's promise as an efficient solution for mitigating high-frequency noise in industrial and environmental applications.

Study on low-mid-frequency broadband sound absorption properties of bending acoustic metamaterials with embedded porous materials

With the rapid development of the traffic industry, noise issues are becoming increasingly serious, and the traditional noise control technologies have the problems of poor low-frequency noise absorption and narrow bandwidth. This study proposes a variable-section bending acoustic metamaterial with an embedded porous material (VS_BAMP). A theoretical model of the VS_BAMP unit is developed based on the Johnson-Champoux-Allard (JCA) model and the impedance transfer method. The sound absorption unit with a thickness of 48 mm exhibits a quasi-perfect (α = 0.98) at 736 Hz, and an efficient sound absorption (α > 0.8) in the range of 574 Hz–966 Hz. Based on the complex frequency plane method, this work designs sound absorption units that exhibit perfect sound absorption at discrete frequencies. By connecting two different absorption units (PVS_BAMP) in parallel, efficient sound absorption from 424 Hz to 1500 Hz is achieved. Finally, the accuracy of the theoretical model is verified by experiments and simulations, confirming the effective sound absorption of PVS_BAMP structure in the middle and low frequency bands. The prepared PVS_BAMP is highly adjustable, has a wide bandwidth, and can be prepared through a simple manufacturing process. Our results can provide a theoretical basis for the design of compact low-mid-frequency broadband noise reduction structures for practical application.

Acoustic and thermal performance of wood strands-rock wool-cement composite boards as eco-friendly construction materials

With a growing emphasis on eco-friendly building materials, wood strands-rock wool-cement boards (WRCB) offer a promising solution due to their composition of renewable wood fibers and cementitious binder. This study delves into the intricate balance between sound absorption, thermal insulation, and cost-effectiveness of WRCB, all of which are crucial factors shaping indoor comfort, energy efficiency, and the economic viability of constructions. WRCB incorporating wood strands, Portland cement, rock wool, and calcium chloride were manufactured at various thicknesses (20–60 mm) and densities (400, 500, and 600 kg/m³). The WRCB exhibited sound absorption average (SAA) and effective thermal conductivity (Keff) values ranging from 0.28 to 0.56 and 0.285–0.381 W/(mK), respectively. These results underscore the remarkable SAA and Keff of the panels, showcasing their potential as sustainable construction materials. Notably, WRCB with thicknesses ranging from 30 mm to 50 mm and densities of 400–500 kg/m³ exhibited nearly ideal absorption levels between 1000 and 2000 Hz, indicating their practical suitability for speech-related scenarios in architectural environments. The utilization of the Response Surface Methodology allowed the optimal conditions for maximizing SAA while simultaneously minimizing Keff and cost to be identified through the optimization process. A density of 452.8 kg/m³ and a thickness of 37.8 mm were determined as the optimal parameters. Under these conditions, the resultant SAA reached 0.554, with a corresponding cost of 1724 IRR and Keff of 0.317 W/(mK). The results also demonstrated the significant impact of thickness and density on the acoustic and thermal performance of the WRCB. The acoustic performance of WRCB was further analyzed through predictive modeling using the Johnson-Champoux-Allard (JCA) and Delany-Bazley (DB) models. The results revealed that the JCA model exhibited superior predictive accuracy compared to the DB model.

From invasive species to bio-based composites: Utilizing water hyacinth for sound absorption and insulation

This study evaluates the acoustic properties of water hyacinth fiber composites, a notorious invasive species, focusing on their effectiveness as sound absorbers and insulators. By converting this abundant weed into bio-based materials, the research presents a promising eco-friendly alternative for acoustic applications. By examining a range of densities from 118 to 282 kg/m³, the study identifies how density impacts the Sound Absorption Coefficient (SAC) and Transmission Loss (TL). Notably, the sample with a density of 118 kg/m³ exhibited the highest Sound Absorption Average (SAA) at 0.63, while the densest sample at 282 kg/m³ achieved the highest Transmission Loss Average (TLA) at 32.5 dB, illustrating a direct correlation between increased density and enhanced TL, alongside a decrease in SAC. Statistical models obtained from the analysis of variance (ANOVA), semi-phenomenological model (Johnson-Champoux-Allard (JCA) model), and empirical model (based on the Delany-Bazley model) have been developed to predict the sound absorption and sound insulation performance of water hyacinth. These models demonstrate average errors of 0.0300, 0.0346, and 0.0377, respectively, significantly outperforming established models like the Delany-Bazley and Garai-Pompoli models in predicting the sound absorption average of water hyacinth bio-based composites. These models can be used to predict the acoustic properties of the composites, assisting in tailoring the composites to match specific applications. This approach leverages the natural abundance of water hyacinth to develop environmentally friendly acoustic materials, offering a sustainable solution to invasive species management and material development.

A machine learning approach for predicting the Johnson-Champoux-Allard parameters of a fibrous porous material

Porous fibrous materials have been widely used as acoustic treatments for noise attenuation. Their acoustic properties are typically characterized by Johnson-Champoux-Allard (JCA) model, which includes five dominant parameters, i.e., open porosity, flow resistivity, tortuosity, viscous characteristic length, and thermal characteristic length. The JCA parameters depend on the microstructure configuration of the material, which can be attained by experimental measurements or numerically analyzing the flow field inside the microstructure, but significant efforts to predict the parameters are typically required. This study proposes a machine learning approach based on an artificial neural network (ANN) for predicting the JCA parameters of a fibrous material. Two geometric parameters that can characterize the fibrous material, i.e., the radius of the fiber and the equivalent throat size between neighbouring fibers, are set as inputs for the prediction model, while the five JCA parameters are set as outputs. The datasets for the network are prepared from finite element simulations. Results confirm that the trained model can predict the JCA parameters accurately and reliably based on the micro-structural geometric parameters. Finally, the model is further validated by the measured acoustic characteristics of a metal-based fibrous material in an impedance tube. The machine learning model opens up possibilities to facilitate the design of advanced porous materials.

Wood chip sound absorbers: Measurements and models

Normal incidence absorption coefficient spectra of samples made from glued wood chips have been measured for various mesh sizes, bulk densities, thicknesses, and air gaps. Increasing thickness introduces additional layer resonance peaks and shifts the initial peak towards lower frequencies. The wood chip samples composed of the smallest mesh sizes were found to offer the highest sound absorption, comparable with that of the same thickness of materials made from synthetic fibers. Measured absorption spectra are compared with predictions of four models for the acoustical properties of rigid porous media. These include a model for slanted parallel identical uniform slits (SS), the Johnson-Champoux-Allard (JCA) and Johnson-Champoux-Allard-Lafarge (JCAL) models for arbitrary pore structures, and model for a non-uniform pore size distribution (NUPSD). Porosity and flow resistivity values have been determined non-acoustically. However, the tortuosity and characteristic lengths required for the JCA model have been obtained by fitting the measured absorption spectra. The thermal permeability required for the JCAL model has been deduced indirectly from the fitted tortuosity through a relationship with standard deviation of the pore size distribution due to the NUPSD model. JCAL and JCA models give the best agreement overall, but predictions of the SS and NUPSD models that use only the fitted tortuosity in addition to measured porosity and flow resistivity are found to give comparable agreement with data for many samples. SS and NUPSD predictions are improved by increasing the tortuosity values compared with those obtained by fitting the JCA model. The study should encourage the creation of sustainable sound-absorbing materials from wood chip wastes.

Acoustic performance of a multi-layer vehicle interior trim sound-absorbing material

This paper investigated the acoustic properties of a multi-layer vehicle interior trim acoustic material. In this study, a thin fiber layer was inserted between the vehicle carpet and the porous sound-absorbing material layer, and backed by the vehicle floor. The theoretical prediction method was based on the Johnson-Champoux-Allard (JCA) model, and the sound absorption coefficient of the multi-layer vehicle interior trim acoustic material was calculated by using the transfer matrix method (TMM). An impedance tube was used to measure the normal incidence sound absorption coefficient of the vehicle interior trim with different combinations of acoustical materials. The comparison of the theoretical and the experimental results showed that the sound absorption coefficient curves predicted using the JCA model are consistent with the measurement data across the mid and high-frequency range (500-6400 Hz). In addition, a car cabin Statistical Energy Analysis (SEA) model was developed and used to predict the effect of the acoustic properties of the multi-layer vehicle interior trim acoustic material. The results presented in this paper, are useful for enhancing vehicle interior acoustic design and refining vehicle cabin sound quality in future vehicles.

Determination of the characteristic impedance and the complex wave number by five different approaches

This study presents several methods to determine the characteristic impedance and the complex wave number of porous materials. These methods include the two-cavity method by Utsuno et al., the two-thickness method by Dunn and Davern, the four-microphone transfer-matrix method by Song and Bolton, the empirical method by Miki and theoretical calculation by the inverse method of Johnson-Champoux-Allard (JCA) model. Glass wool and polyester materials are used to compare different approaches. The results indicate a clear correlation among all methods. The theoretical calculation based on the inverse method of the JCA model can predict well without direct measurement of the non-acoustical parameters. However, there are disadvantages to several methods including sharp peaks in some frequency ranges for the two-cavity method, data fluctuation at low frequency for the four-microphone method, and inaccuracy in predicting the characteristic impedance of thick materials at middle and high frequencies for the two-thickness method. The results imply that the inverse method of the JCA model is a reliable method to predict the characteristic impedance and the complex wave number with comparable accuracy to traditional experimental methods.

Comparing acoustic prediction methods for additively manufactured porous structures

While macroscale methods for predicting the acoustic properties of porous structures have been popular in the past, they often require time-consuming manufacturing and testing workflows. Meanwhile, microscale approaches allow the prediction of transport parameters based exclusively on a periodic structure's unit cell geometry. Here, we compare these methods to predict the characteristic impedance of additively manufactured porous structures. We use the microscale approach to estimate the geometry's transport parameters, then predict the characteristic properties using the Johnson-Champoux-Allard (JCA) model. We measure the acoustic properties of the printed structures using a normal incidence impedance tube and estimate the transport parameters using an inverse characterization approach. We use the twothickness method as a macroscale approach to predict the characteristic properties from the measured surface impedances of two sample thicknesses. Finally, we compare these characteristic prediction methods. Our results show that the inverse characterization and twothickness methods offer the closest match to the measured values at low frequencies.

Acoustic characterization of natural areca catechu fiber‐reinforced flexible polyurethane foam composites

AbstractThe development of acoustic absorbers from natural resources is a novel approach in acoustics. In the current study, the effect of unprocessed raw areca fiber (AF) particle reinforcement on the sound absorption (SA) behavior of polyurethane (PU) foam composites is investigated. Influences of fiber weight percentage and graded distribution of fiber with varying fiber weight percentage on the SA coefficient (SAC) of the composite foams are examined through the impedance tube approach. Morphological studies are carried out with the help of FESEM images to investigate the acoustic energy dissipation mechanism of PU foam and its composites. It is found that the SA capability of the composite foam is enhanced by increased fiber weight percentage, graded distribution of fiber wt%, varying sample thickness, and air cavity length. In general, PU‐AF composite specimens show a peak SA value of 0.95 around 450 Hz, which is not the case for other natural fiber results available in the literature. Theoretical results predicted using the JCA (Johnson‐Champoux Allard) model agree with the experimental results.

Theoretical study of sound absorption performance for Al2O3-polyurethane foam composite

To reveal the sound absorption property and mechanisms of Al2O3-polyurethane (Al2O3-PU) composite materials in theory, the impedance tube model is established by using the finite element method in this paper. Meantime, the theoretical modal and the calculation method adopt the Johnson-Champoux-Allard (JCA) modal and the standing wave ratio method, respectively. By comparing the experimental and simulation values of PU and Al2O3-PU composites from 0 to 1250 Hz, the results prove that the JCA predicted values can match the actual measured values. To further predict the sound property of Al2O3-PU composites in the medium and high frequency, the diameter of 29 mm and length of 600 mm for the impedance tube is installed. Finally, the sound pressure distribution diagram of the impedance tube is obtained.

On Woven Fabric Sound Absorption Prediction

For building applications, woven fabrics have been widely used as finishing elements of room interior but not in particular aimed for sound absorbers. Considering the micro perforation of the woven fabrics, they should have potential to be used as micro-perforated panel (MPP) absorbers; some measurement results indicated such absorption ability. Hence, it is of importance to have a sound absorption model of the woven fabrics to enable us predicting their sound absorption characteristic that is beneficial in engineering design phase. Treating the woven fabric as a rigid frame, a fluid equivalent model is employed based on the formulation of Johnson-Champoux-Allard (JCA). The model obtained is then validated by measurement results where three kinds of commercially available woven fabrics are evaluated by considering their perforation properties. It is found that the model can reasonably predict their sound absorption coefficients. However, the presence of perturbations in pores give rise to inaccuracy of resistive component of the predicted surface impedance. The use of measured static flow resistive and corrected viscous length in the calculations are useful to cope with such a situation. Otherwise, the use of an optimized simple model as a function of flow resistivity is also applicable for this case.

Nonwoven fabrics developed from agriculture and industrial waste for acoustic and thermal applications

Numerous researchers in the field of noise control and acoustics have found success in using biomaterials to create a porous sound absorber that is both effective and environmentally friendly. This paper discusses the utilization of fibers extracted from the waste from coffee husk (CH) and waste from the cotton (CO) spinning industry to be an alternative to synthetic-based acoustic materials. The study was conducted within the range of 50–6300 (Hz) frequency. Five well-known mathematical prediction models, namely Delany–Bazley (D–B), Garai–Pompoli (G–P), Miki, Allard Champoux (AC), and Johnson–Champoux–Allard (JCA) models are theoretically used to predict the sound absorption coefficient of nonwoven fibrous materials. When compared with the experimental data, it was discovered that the JCA and AC model is the most acceptable model for predicting the absorption behavior of CH/CO nonwoven fibrous materials. Additionally, the thermal insulation of nonwoven fibrous materials has been experimentally and numerically studied. It is noteworthy that, when compared to the Herman model, the Bhattacharyya model’s results showed slightly greater thermal conductivities. Overall, this work used an environmentally friendly way to turn waste into a valuable product.

Experimental and Semi-Phenomenological Investigation on Sound Absorption Performance of Natural Granular Sound Absorber: A Case Study on Rice Bran Composites

The sound absorption performance of rice bran composites was quantitatively investigated through an improved semi-phenomenological approach. Rice bran (RB) was employed as the primary and structural component in the creation of granular-type sound absorbers with urea-formaldehyde (UF) adhesive. The sound absorption coefficient (SAC) was measured by the two-microphone impedance tube method. Samples with a rice bran per volume ratio lower than 253 kg/m3 show peak-valley characteristics in the saturation region of their SAC spectrum. Five non-acoustic parameters for each sample were obtained by direct measurement and fitting the experimental SACs to the semi-phenomenological Johnson-Champoux-Allard (JCA) equivalent fluid model using the least-squares fitting method. Samples with higher proportions of RB demonstrate lower porosity (ϕ), viscous characteristic length (Λ), and thermal characteristic length (Λ'). Flow resistivity (σ) was the only parameter that noticeably increases when RB increased while tortuosity (α_∞) did not show a strong correlation. For the uncertainty analysis of the experimental SAC, multivariate method was used in this study. A new model (NM) was predicated on the power-law relation introduced by Delany and Bazley, in which the SAC was a function of flow resistivity alone. The new model predicted the SAC of RB composites more precisely than the standard Delany-Bazley model (¯Δ_"abs(DBM)" ≈4.0¯Δ_"abs(NM)" ). The proposed model had the potential to be extended into a more unified empirical model of SAC for granular-typed sound absorbers in future investigations with a broader spectrum of granular materials.

Optimization design of the sound absorbing structure of double-layer porous metal material with air layer based on genetic algorithm.

An acoustic absorption structure of a double-layer porous metal material with air layers is proposed. The Johnson-Champoux-Allard (JCA) model combined with the transfer matrix method (TMM) was used to establish the theoretical calculation model of the sound absorption coefficient (SAC). Meanwhile, the SAC between 500 and 6300 Hz were measured with an impedance tube. The errors between the theoretical and experimental values were compared to illustrate the good predictability of the theoretical model within the inverse estimations of the transport properties. The effects of the material placement order, material thickness, and cavity depth on the sound absorption performance from 200 to 5000 Hz were analyzed using the theoretical model. Further, a multi-objective function genetic algorithm was used to optimize the porous material's thickness and SAC to obtain an acoustic structure with a smaller thickness and higher sound absorption. A series of optimal solutions were obtained for acoustic structures with a total thickness of less than 70 mm. When the total thickness of the foam metal was 33.57 mm, the average SAC reached 0.853, which was significantly lower than the total thickness of the previous experiments. The multi-objective function genetic algorithm can provide a reliable solution for the optimal design of most sound-absorbing structures.

Optimization and modeling of the sound absorption behavior of polyurethane composite foams reinforced with kenaf fiber

In this study, hybrid composites made of polyurethane (PU) matrix and natural kenaf fiber filler were designed and fabricated. The Response Surface Methodology (RSM) optimization approach coupled with a Central Composite Design (CCD) was used to design the experiments and optimize the parameters affecting the acoustic performance of the samples in the low and mid-frequency ranges. The acoustic performance of the samples was investigated using the impedance tube method. Additionally, the acoustic performance was predicted using a developed Quadratic model as well as the Delany-Bazley (DB) and Johnson-Champoux-Allard (JCA) models. The results showed that the acoustic absorption of neat PU foam is remarkably improved at all frequencies with the addition of kenaf fibers as the filler. It was found that the acoustic performance of the composite samples is optimal when the added amount of kenaf fibers to the polymer matrix and fiber length are 1.2 wt% and 8 mm, respectively. The sound absorption average (SAA) of the optimized composite and neat PU foam was calculated as 0.65 and 0.48, respectively i.e., an increase of 35.4 %. The Quadratic model showed very high accuracy for the prediction of the SAA of the optimized sample. The JCA model provided higher accuracy for the prediction of the frequency-dependent sound absorption coefficient of the composites as compared with the DB model.

Experimental and Numerical Investigation of Sound Absorption Characteristics of Rebonded Polyurethane Foam

Polyurethane foam (PUF) is an exceptionally adaptable product that has a variety of applications—it can be found almost everywhere. Due to such high utilization, the amount of polyurethane foam waste generated each year is growing over time. Rebonding polyurethane foam waste is a suitable way to progress towards a circular economy. In this paper, the prospect of using rebonded polyurethane foam (RPUF) in noise control applications is examined. An experimental study was carried out on RPUFs with various thicknesses and densities. The sound absorption coefficients at normal incidence and air resistivity were measured. The five-parameter Johnson-Champoux-Allard (JCA) model was adopted for the simulation of the porous layer. The remaining unknown parameters of the JCA model were estimated by inverse acoustic characterization based on fitting the transfer matrix method (TMM) model of an unbounded porous layer with rigid backing to the experimentally obtained sound absorption coefficients. Furthermore, sound absorption coefficients were calculated for a wide range of sample thicknesses, as well as for different air gap thicknesses between the wall and the porous layer. For some of the considered RPUFs, a sound absorption coefficient above 0.8 was achieved over a wide frequency range.

Effects of urea–formaldehyde and polyvinyl acetate adhesive on sound absorption coefficient and sound transmission loss of palmyra palm fruit fiber composites

Composite materials made from palmyra palm fruit fiber (PPFF) were formed using urea–formaldehyde (UF) and polyvinyl acetate (PVAc). We prepared two sets of five different cylindrical samples with varying PPFF contents. The PPFF composites’ normal-incident sound absorption coefficient (SAC) and transmission loss (TL) were measured by using the impedance tube method. The sample with higher PPFF content shows a lower SAC spectrum. It is the opposite for the TL, where a sample with high PPFF content demonstrates a higher TL spectrum. We conducted the least-square fitting method on the experimental SAC and TL spectra utilizing the Johnson-Champoux-Allard (JCA) equivalent fluid model. Non-acoustic parameters were acquired from the fitting. The optimized porosity (ϕ), viscous characteristic length (Λ), and thermal characteristic length (Λ′) are inversely proportional to the PPFF content. The airflow resistivity (σ) and tortuosity (α∞), on the other hand, demonstrate a direct correlation with the PPFF content. Even though the UF samples have an average density of 14.7 % higher than the PVAc samples, their σ,Λ′, and α∞ are just 7.7 %, 4.5 %, and 0.39 % higher than PVAc samples. On the other hand, PVAc samples show higher average Λ and ϕ of 1.4 % and 0.73 %, respectively. The optimized porosity values obtained from the JCA model (ϕJCA) are coherent with ones from the direct estimation method assuming adhesive-coated fiber (ϕfa). It can be concluded that the adhesive’s quantity and density contribute to the composites’ porosity value, ultimately affecting material acoustic properties. Researchers can control and predict how the SAC and TL of fibrous sound absorbers would behave by varying the quantity and density of an adhesive.

Waste Durian Husk Fibers as Natural Sound Absorber: Performance and Acoustic Characterization

The paper presents the sound absorption coefficient of acoustic absorbers fabricated from natural durian husk fibers, which are currently still considered as agricultural wastes, especially in Malaysia. Samples were fabricated with different fiber densities and thicknesses and the sound absorption performance was measured using the impedance tube method. The results reveal that the durian husk fibers can have absorption coefficient of more than 0.5 above 1 kHz for a minimum thick sample of 20 mm and with minimum density of 160 kg/m3. The optimised macroscopic parameters for various densities were calculated using the inverse method employing the well-known Johnson-Champoux-Allard (JCA) model for porous material.