Study on the effects of denier and shapes of polyester fibres on acoustic performance of needle-punched nonwovens with air-gap: comparison of artificial neural network and regression modelling approaches to predict the sound absorption coefficient of nonwovens

2018 ◽  
Vol 110 (5) ◽  
pp. 715-723 ◽  
Author(s):  
Madaswamy Ramamoorthy ◽  
Raju Seenivasan Rengasamy
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Absorption Coefficient ◽  
Sound Absorption ◽  
Air Gap ◽  
Sound Absorption Coefficient ◽  
Acoustic Performance ◽  
Regression Modelling ◽  
Artificial Neural ◽  
Modelling Approaches

scholarly journals Sound Absorption Performance of Sugar Palm Trunk Fibers

2019 ◽  
Vol 130 ◽  
pp. 01003
Author(s):  
Anditya Endar Prabowo ◽  
Kuncoro Diharjo ◽  
Ubaidillah ◽  
Iwan Prasetiyo
Keyword(s):  
Absorption Coefficient ◽  
Bulk Density ◽  
Sound Absorption ◽  
Random Noise ◽  
Air Gap ◽  
Sound Absorption Coefficient ◽  
Low Frequencies ◽  
Central Java ◽  
Absorption Performance ◽  
Small Medium Enterprises

The purpose of this research is to investigate the effect of bulk density, thickness, and air gap to sound absorption performance on absorber based sugar palm trunk fibers. The fibers were obtained from solid waste on Small-Medium Enterprises of sago flour processing in Klaten, Central Java, Indonesia. The absorber specimens were formed from the fibers using a simple press molding in an oven at 150 °C. According to ISO 10534-2, the absorber samples were tested using two microphones impedance tube with random noise source to get the curve of the sound absorption coefficient. The result shows that the absorption performance can be improved by increasing bulk density and increasing of sample thickness. Especially at low frequencies, improvement of the sound absorption coefficient can be achieved (NAC > 0.8) by applying the air gap behind the sample. The best performance of absorber based sugar palm trunk fiber can be achieved for (1 000 to 6 000) Hz range frequency.

scholarly journals Elucidating the Sound Absorption Characteristics of Foxtail Millet (Setariaitalica) Husk

Materials ◽  
2020 ◽  
Vol 13 (22) ◽  
pp. 5126
Author(s):  
Dhayalini Balasubramanian ◽  
Senthil Rajendran ◽  
Bhuvanesh Srinivasan ◽  
Nirmalakumari Angamuthu
Keyword(s):  
Absorption Coefficient ◽  
Sound Absorption ◽  
Foxtail Millet ◽  
Air Gap ◽  
Composite Panel ◽  
Sound Absorption Coefficient ◽  
Mass Content ◽  
Frequency Range ◽  
Backing Material ◽  
Absorption Characteristics

The current study deals with the analysis of sound absorption characteristics of foxtail millet husk powder. Noise is one the most persistent pollutants which has to be dealt seriously. Foxtail millet is a small seeded cereal cultivated across the world and its husk is less explored for its utilization in polymer composites. The husk is the outer protective covering of the seed, rich in silica and lingo-cellulose content making it suitable for sound insulation. The acoustic characterization is done for treated foxtail millet husk powder and polypropylene composite panels. The physical parameters like fiber mass content, density, and thickness of the composite panel were varied and their influence over sound absorption was mapped. The influence of porosity, airflow resistance, and tortuosity was also studied. The experimental result shows that 30-mm thick foxtail millet husk powder composite panel with 40% fiber mass content, 320 kg/m3 density showed promising sound absorption for sound frequency range above 1000 Hz. We achieved noise reduction coefficient (NRC) value of 0.54. In view to improve the performance of the panel in low-frequency range, we studied the efficiency of incorporating air gap and rigid backing material to the designed panel. We used foxtail millet husk powder panel of density 850 kg/m3 as rigid backing material with varying air gap thickness. Thus the composite of 320 kg/m3 density, 30-mm thick when provided with 35-mm air gap and backing material improved the composite’s performance in sound frequency range 250 Hz to 1000 Hz. The overall sound absorption performance was improved and the NRC value and average sound absorption coefficient (SAC) were increased to 0.7 and 0.63 respectively comparable with the commercial acoustic panels made out of the synthetic fibers. We have calculated the sound absorption coefficient values using Delany and Bezlay model (D&B model) and Johnson–Champoux–Allard model (JCA model) and compared them with the measured sound absorption values.

Acoustic Benefits of Ecofriendly Spent Tea Leaves Filled Porous Material

2017 ◽  
Vol 739 ◽  
pp. 125-134
Author(s):  
Kylie Wong ◽  
Qumrul Ahsan ◽  
Azma Putra ◽  
Sivarao Subramonian ◽  
Noraiham Mohamad ◽  
...  
Keyword(s):  
Absorption Coefficient ◽  
High Resistance ◽  
Sound Absorption ◽  
Transmission Loss ◽  
Acoustic Property ◽  
Tea Plant ◽  
Sound Absorption Coefficient ◽  
Acoustic Performance ◽  
Absorbing Material ◽  
Tea Leaf

This paper demonstrates the feasibility of spent tea leaf (STL) fiber as an eco-friendly sound absorbing material. STL fiber is a by-product which was extracted from tea plant. STL are rich in polyphenols (tannins) which cause high resistance to fungal and termites, and high resistance to fire. In addition, STL are hollow and cellular in nature and thus perform well as acoustic and thermal insulators. Three different grades of STL were studied and the acoustic property was analyzed in terms of sound absorption coefficient and transmission loss. Experimental measurements of sound absorption coefficient in impedance tube are conducted. It was found that finest STL fiber grade exhibits better acoustic performance among others. Furthermore, the effect of latex binder on the acoustic property of STL fiber was also analyzed. Results suggest that the types of binder such as polyurethane and latex influenced the acoustic performance of STL fiber.

scholarly journals STATIC AIR-GAP ECCENTRICITY FAULT DETECTION OF INDUCTION MOTOR USING ARTIFICIAL NEURAL NETWORK (ANN)

2020 ◽  
Vol 8 (8) ◽  
pp. 377-385
Author(s):  
KHALED MOHAMMED BIR GAMAL ◽  
SUPRIYA P. PANDA ◽  
M. V. RAMANA MURTHY
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Detection ◽  
Induction Motor ◽  
Back Propagation ◽  
Air Gap ◽  
Operating Conditions ◽  
Detection Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Induction motor plays an important role in the industrial, commercial and residential industries, owing to its immense advantages over the opposite types of motors. Such motors have to operate under different operating conditions that cause engine degradation leading to fault occurrences. There are numerous fault detection techniques available. There are numerous fault detection techniques available. The technique used in this paper to prove the effect of static air gap eccentricity on behaving or performing of the three-phase induction motor is the artificial neural network (ANN) as ANN depends on detecting the fault on the amplitude of positive and negative harmonics of frequencies. In this paper, we used two motors to achieve real malfunctions and to get the required data and for three different load tests. In this paper, we adopted MCSA to detect the fault based on the stator current. The ANN training algorithm used in this paper is back propagation and feed forward. The inputs of ANN are the speed and the amplitudes of the positive and the negative harmonics, and the type of fault is the output. To distinguish between healthy and faulty motor, the input data of ANN are well-trained via experiments test. The methodology applied in this paper was MATLAB and present how we can distinguish between healthy and faulty motor.

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