In this research, we developed numerically a Prototype Auditory Membrane (PAM) for a fully implantable and self contained artificial cochlea. Cochleae are one of the important organs for hearing in the human and animals. Material of the prototype and implant of PAM are made of Polyvinylidene fluoride (PVDF)- Kureha, Japan which is fabricated using MEMS and thin film technologies. Another important thing in the characteristic of the PAM is not only convert the acoustic wave into electric signal but also the frequency selectivity. The thickness, Young’s modulus and density of the PAM are 40 μm, 4 GPa, and 1.79 103 kg/m3, respectively. The shape and dimension of the PAM is trapezoidal with the width is linearly changed from 2.0 to 4.0 mm with the length are 30 mm. Numerically, we develop the model of PAM is based on commercial CFD software, Fluent 6.3.26 and Gambit 2.4.6. The geometry model of the PAM consists of one-sided blocks of quadrilateral elements for 2D model and tetrahedral elements for 3 D model respectively. In this study we set the flow as laminar and carried out using unsteady time dependent calculation. The results show that the frequency selectivity of the membrane is detected on the membrane surface.
Analytical framework for optimized feature extraction for upgrading occupancy...IJECEIAES
The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...IJECEIAES
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
Development of real-time indoor human tracking system using LoRa technology IJECEIAES
Industrial growth has increased the number of jobs hence increase thenumber of employees. Therefore, it is impossible to track the location of allemployees in the same building at the same time as they are placed in adifferent department. In this work, a real-time indoor human tracking systemis developed to determine the location of employees in a real-timeimplementation. In this work, the long-range (LoRa) technology is used asthe communication medium to establish the communication between thetracker and the gateway in the developed system due to its low power withhigh coverage range besides requires low cost for deployment. The receivedsignal strength indicator (RSSI) based positioning method is used to measurethe power level at the receiver which is the gateway to determine thelocation of the employees. Different scenarios have been considered toevaluate the performance of the developed system in terms of precision andreliability. This includes the size of the area, the number of obstacles in theconsidered area, and the height of the tracker and the gateway. A real-timetestbed implementation has been conducted to evaluate the performance ofthe developed system and the results show that the system has high precisionand are reliable for all considered scenarios.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Survey on deep learning applied to predictive maintenance IJECEIAES
Prognosis health monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Analytical framework for optimized feature extraction for upgrading occupancy...IJECEIAES
The adoption of the occupancy sensors has become an inevitable in commercial and non-commercial security devices, owing to their proficiency in the energy management. It has been found that the usages of conventional sensors is shrouded with operational problems, hence the use of the Doppler radar offers better mitigation of such problems. However, the usage of Doppler radar towards occupancy sensing in existing system is found to be very much in infancy stage. Moreover, the performance of monitoring using Doppler radar is yet to be improved more. Therefore, this paper introduces a simplified framework for enriching the event sensing performance by efficient selection of minimal robust attributes using Doppler radar. Adoption of analytical methodology has been carried out to find that different machine learning approaches could be further used for improving the accuracy performance for the feature that has been extracted in the proposed system of occuancy system.
Applications of Artificial Neural Networks in Civil EngineeringPramey Zode
An artificial brain-like network based on certain mathematical algorithms developed using a numerical computing environment is called as an ‘Artificial Neural Network (ANN)’. Many civil engineering problems which need understanding of physical processes are found to be time consuming and inaccurate to evaluate using conventional approaches. In this regard, many ANNs have been seen as a reliable and practical alternative to solve such problems. Literature review reveals that ANNs have already being used in solving numerous civil engineering problems. This study explains some cases where ANNs have been used and its future scope is also discussed.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Acoustic Scene Classification by using Combination of MODWPT and Spectral Fea...ijtsrd
Acoustic Scene Classification ASC is classified audio signals to imply about the context of the recorded environment. Audio scene includes a mixture of background sound and a variety of sound events. In this paper, we present the combination of maximal overlap wavelet packet transform MODWPT level 5 and six sets of time domain and frequency domain features are energy entropy, short time energy, spectral roll off, spectral centroid, spectral flux and zero crossing rate over statistic values average and standard deviation. We used DCASE Challenge 2016 dataset to show the properties of machine learning classifiers. There are several classifiers to address the ASC task. We compare the properties of different classifiers K nearest neighbors KNN , Support Vector Machine SVM , and Ensembles Bagged Trees by using combining wavelet and spectral features. The best of classification methodology and feature extraction are essential for ASC task. In this system, we extract at level 5, MODWPT energy 32, relative energy 32 and statistic values 6 from the audio signal and then extracted feature is applied in different classifiers. Mie Mie Oo | Lwin Lwin Oo "Acoustic Scene Classification by using Combination of MODWPT and Spectral Features" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd27992.pdfPaper URL: https://www.ijtsrd.com/computer-science/multimedia/27992/acoustic-scene-classification-by-using-combination-of-modwpt-and-spectral-features/mie-mie-oo
Solution for intra/inter-cluster event-reporting problem in cluster-based pro...IJECEIAES
In recent years, wireless sensor networks (WSNs) have been considered one of the important topics for researchers due to their wide applications in our life. Several researches have been conducted to improve WSNs performance and solve their issues. One of these issues is the energy limitation in WSNs since the source of energy in most WSNs is the battery. Accordingly, various protocols and techniques have been proposed with the intention of reducing power consumption of WSNs and lengthen their lifetime. Cluster-oriented routing protocols are one of the most effective categories of these protocols. In this article, we consider a major issue affecting the performance of this category of protocols, which we call the intra/inter-cluster event-reporting problem (IICERP). We demonstrate that IICERP severely reduces the performance of a cluster-oriented routing protocol, so we suggest an effective Solution for IICERP (SIICERP). To assess SIICERP’s performance, comprehensive simulations were performed to demonstrate the performance of several cluster-oriented protocols without and with SIICERP. Simulation results revealed that SIICERP substantially increases the performance of cluster-oriented routing protocols.
Development of real-time indoor human tracking system using LoRa technology IJECEIAES
Industrial growth has increased the number of jobs hence increase thenumber of employees. Therefore, it is impossible to track the location of allemployees in the same building at the same time as they are placed in adifferent department. In this work, a real-time indoor human tracking systemis developed to determine the location of employees in a real-timeimplementation. In this work, the long-range (LoRa) technology is used asthe communication medium to establish the communication between thetracker and the gateway in the developed system due to its low power withhigh coverage range besides requires low cost for deployment. The receivedsignal strength indicator (RSSI) based positioning method is used to measurethe power level at the receiver which is the gateway to determine thelocation of the employees. Different scenarios have been considered toevaluate the performance of the developed system in terms of precision andreliability. This includes the size of the area, the number of obstacles in theconsidered area, and the height of the tracker and the gateway. A real-timetestbed implementation has been conducted to evaluate the performance ofthe developed system and the results show that the system has high precisionand are reliable for all considered scenarios.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
AN IMPROVED METHOD FOR IDENTIFYING WELL-TEST INTERPRETATION MODEL BASED ON AG...IAEME Publication
This paper presents an approach based on applying an aggregated predictor formed by multiple versions of a multilayer neural network with a back-propagation optimization algorithm for helping the engineer to get a list of the most appropriate well-test interpretation models for a given set of pressure/ production data. The proposed method consists of three stages: (1) data decorrelation through principal component analysis to reduce the covariance between the variables and the dimension of the input layer in the artificial neural network, (2) bootstrap replicates of the learning set where the data is repeatedly sampled with a random split of the data into train sets and using these as new learning sets, and (3) automatic reservoir model identification through aggregated predictor formed by a plurality vote when predicting a new class. This method is described in detail to ensure successful replication of results. The required training and test dataset were generated by using analytical solution models. In our case, there were used 600 samples: 300 for training, 100 for cross-validation, and 200 for testing. Different network structures were tested during this study to arrive at optimum network design. We notice that the single net methodology always brings about confusion in selecting the correct model even though the training results for the constructed networks are close to 1. We notice also that the principal component analysis is an effective strategy in reducing the number of input features, simplifying the network structure, and lowering the training time of the ANN. The results obtained show that the proposed model provides better performance when predicting new data with a coefficient of correlation approximately equal to 95% Compared to a previous approach 80%, the combination of the PCA and ANN is more stable and determine the more accurate results with lesser computational complexity than was feasible previously. Clearly, the aggregated predictor is more stable and shows less bad classes compared to the previous approach.
Survey on deep learning applied to predictive maintenance IJECEIAES
Prognosis health monitoring (PHM) plays an increasingly important role in the management of machines and manufactured products in today’s industry, and deep learning plays an important part by establishing the optimal predictive maintenance policy. However, traditional learning methods such as unsupervised and supervised learning with standard architectures face numerous problems when exploiting existing data. Therefore, in this essay, we review the significant improvements in deep learning made by researchers over the last 3 years in solving these difficulties. We note that researchers are striving to achieve optimal performance in estimating the remaining useful life (RUL) of machine health by optimizing each step from data to predictive diagnostics. Specifically, we outline the challenges at each level with the type of improvement that has been made, and we feel that this is an opportunity to try to select a state-of-the-art architecture that incorporates these changes so each researcher can compare with his or her model. In addition, post-RUL reasoning and the use of distributed computing with cloud technology is presented, which will potentially improve the classification accuracy in maintenance activities. Deep learning will undoubtedly prove to have a major impact in upgrading companies at the lowest cost in the new industrial revolution, Industry 4.0.
Fault diagnosis of a high voltage transmission line using waveform matching a...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Fault Diagnosis of a High Voltage Transmission Line Using Waveform Matching A...ijsc
This paper is based on the problem of accurate fault diagnosis by incorporating a waveform matching technique. Fault isolation and detection of a double circuit high voltage power transmission line is of immense importance from point of view of Energy Management services. Power System Fault types namely single line to ground faults, line to line faults, double line to ground faults etc. are responsible for transients in current and voltage waveforms in Power Systems. Waveform matching deals with the approximate superimposition of such waveforms in discretized versions obtained from recording devices and Software respectively. The analogy derived from these waveforms is obtained as an error function of voltage and current, from the considered metering devices. This assists in modelling the fault identification as an optimization problem of minimizing the error between these sets of waveforms. In other words, it utilizes the benefit of software discrepancies between these two waveforms. Analysis has been done using the Bare Bones Particle Swarm Optimizer on an IEEE 2 bus, 6 bus and 14 bus system. The performance of the algorithm has been compared with an analogous meta-heuristic algorithm called BAT optimization on a 2 bus level. The primary focus of this paper is to demonstrate the efficiency of such methods and state the common peculiarities in measurements, and the possible remedies for such distortions.
Parallel implementation of pulse compression method on a multi-core digital ...IJECEIAES
Pulse compression algorithm is widely used in radar applications. It requires a huge processing power in order to be executed in real time. Therefore, its processing must be distributed along multiple processing units. The present paper proposes a real time platform based on the multi-core digital signal processor (DSP) C6678 from Texas Instruments (TI). The objective of this paper is the optimization of the parallel implementation of pulse compression algorithm over the eight cores of the C6678 DSP. Two parallelization approaches were implemented. The first approach is based on the open multi processing (OpenMP) programming interface, which is a software interface that helps to execute different sections of a program on a multi core processor. The second approach is an optimized method that we have proposed in order to distribute the processing and to synchronize the eight cores of the C6678 DSP. The proposed method gives the best performance. Indeed, a parallel efficiency of 94% was obtained when the eight cores were activated.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
APPROXIMATE ARITHMETIC CIRCUIT DESIGN FOR ERROR RESILIENT APPLICATIONSVLSICS Design
When the application context is ready to accept different levels of exactness in solutions and is supported
by human perception quality, then the term ‘Approximate Computing’ tossed before one decade will
become the first priority . Even though computer hardware and software are working to generate exact
results, approximate results are preferred whenever an error is in predefined bound and adaptive. It will
reduce power demand and critical path delay and improve other circuit metrics. When it comes to
traditional arithmetic circuits, those generating correct results with limitations on performance are rapidly
getting replaced by approximate arithmetic circuits which are the need of the hour, and so on about their
design.
APPROXIMATE ARITHMETIC CIRCUIT DESIGN FOR ERROR RESILIENT APPLICATIONSVLSICS Design
When the application context is ready to accept different levels of exactness in solutions and is supported
by human perception quality, then the term ‘Approximate Computing’ tossed before one decade will
become the first priority . Even though computer hardware and software are working to generate exact
results, approximate results are preferred whenever an error is in predefined bound and adaptive. It will
reduce power demand and critical path delay and improve other circuit metrics. When it comes to
traditional arithmetic circuits, those generating correct results with limitations on performance are rapidly
getting replaced by approximate arithmetic circuits which are the need of the hour, and so on about their
design.
Gene's law, Common gate, kernel Principal Component Analysis, ASIC Physical Design Post-Layout Verification, TSMC180nm, 0.13um IBM CMOS technology, Cadence Virtuoso, FPAA, in Spanish, Bruun E,
Intelligent Arabic letters speech recognition system based on mel frequency c...IJECEIAES
Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
Embedded system projects for final year BangaloreAidell2583
Final Year Engineering Projects For ECE in Bangalore. Embedded Innovation Lab is the right place for best real time final year engineering projects. EIL is not a training institute its a embedded company.You can learn new ideas and insdustrial working experience. BE/B.Tech student projects in bangalore,mechanical final year project in bangalore
Recent Trends InDigital Differential Protection of Power Transformerijiert bestjournal
Digital protection has several advantages over conventional protection scheme. For protecting
costliest and vital equipment such as transformer, digital schemes have been proposed by several authors in recent
past. This paper throws light on all such efforts and it will help researchers to focus on integrated efforts to protect
transformer in a better and efficient way. Artificial intelligence along with signature and pattern recognition
techniques give much more useful information about happenings in and outside of transformer. Efforts are put by
all concerned with fast, accurate, flexible, reliable and easy to understand scheme of protection. With the advent of
soft computing methods condition monitoring with protection has become on line objective. Keeping all these
state of art techniques of protection, this paper will be a useful resource. Discrimination of several faults external
and internal needs digital signal processing and feature extraction as well. Many algorithms are proposed as
summarized in paper.
Similar to NUMERICAL STUDIES OF TRAPEZOIDAL PROTOTYPE AUDITORY MEMBRANE (PAM) (20)
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptxR&R Consult
CFD analysis is incredibly effective at solving mysteries and improving the performance of complex systems!
Here's a great example: At a large natural gas-fired power plant, where they use waste heat to generate steam and energy, they were puzzled that their boiler wasn't producing as much steam as expected.
R&R and Tetra Engineering Group Inc. were asked to solve the issue with reduced steam production.
An inspection had shown that a significant amount of hot flue gas was bypassing the boiler tubes, where the heat was supposed to be transferred.
R&R Consult conducted a CFD analysis, which revealed that 6.3% of the flue gas was bypassing the boiler tubes without transferring heat. The analysis also showed that the flue gas was instead being directed along the sides of the boiler and between the modules that were supposed to capture the heat. This was the cause of the reduced performance.
Based on our results, Tetra Engineering installed covering plates to reduce the bypass flow. This improved the boiler's performance and increased electricity production.
It is always satisfying when we can help solve complex challenges like this. Do your systems also need a check-up or optimization? Give us a call!
Work done in cooperation with James Malloy and David Moelling from Tetra Engineering.
More examples of our work https://www.r-r-consult.dk/en/cases-en/
CFD Simulation of By-pass Flow in a HRSG module by R&R Consult.pptx
NUMERICAL STUDIES OF TRAPEZOIDAL PROTOTYPE AUDITORY MEMBRANE (PAM)
1. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
DOI : 10.5121/ijcsea.2012.2404 25
NUMERICAL STUDIES OF TRAPEZOIDAL
PROTOTYPE AUDITORY MEMBRANE (PAM)
Harto Tanujaya1
, Satoyuki Kawano2
1
Department of Mechanical Engineering, Tarumanagara University, Jakarta, Indonesia
hart_tan18@yahoo.com
2
Mechanical Science and Bioengineering Department, Osaka University, Japan
ABSTRACT
In this research, we developed numerically a Prototype Auditory Membrane (PAM) for a fully implantable
and self contained artificial cochlea. Cochleae are one of the important organs for hearing in the human
and animals. Material of the prototype and implant of PAM are made of Polyvinylidene fluoride (PVDF)-
Kureha, Japan which is fabricated using MEMS and thin film technologies. Another important thing in the
characteristic of the PAM is not only convert the acoustic wave into electric signal but also the frequency
selectivity. The thickness, Young’s modulus and density of the PAM are 40 μm, 4 GPa, and 1.79 103
kg/m3
, respectively. The shape and dimension of the PAM is trapezoidal with the width is linearly changed
from 2.0 to 4.0 mm with the length are 30 mm. Numerically, we develop the model of PAM is based on
commercial CFD software, Fluent 6.3.26 and Gambit 2.4.6. The geometry model of the PAM consists of
one-sided blocks of quadrilateral elements for 2D model and tetrahedral elements for 3 D model
respectively. In this study we set the flow as laminar and carried out using unsteady time dependent
calculation. The results show that the frequency selectivity of the membrane is detected on the membrane
surface.
KEYWORDS
Cochlea, PVDF, PAM, Frequency Selectivity
1. INTRODUCTION
As a medical treatment for sensorineural hearing loss in children and adults, cochlear implant is
recently used [1][2]. The current cochlear implant consist of an implant stimulating electrodes
and an extracorporeal device, which bypass the damaged hair cells by generating electric current
in response to the acoustical sound [3]. There is a battery inside the extracorporeal device, which
is one of the critical disadvantages in the current cochlear implant. This status motivates us to
develop a fully self-contained artificial cochlea.
The function and geometrical of the cochlea has been being studied by many researchers in the
world. Measuring and modelling the geometric of the cochlea and biological basilar membrane
technically are very complicated, these become a challenge to be researched. In this research we
use the Prototype Auditory Membrane (PAM) as the simplifying model of the biological basilar
membrane. The important role of the cochlea is not only conversion of acoustical sound to
2. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
26
electrical signals but also frequency selectivity [4]. Numerical simulation is one of tools to predict
the frequency selectivity on it. The simulation of fluid flow is an application of numerical method
for estimating the pattern of fluid flow distribution as well as on cochlea. These methods describe
the prediction of fluid flow process and its distribution, before and after a real fluid flow process
happened [5]. The information about fluid flow distribution with certain frequency are utilized to
create a pressure distribution area map and fluid flow activity. Generally in this research, we have
studied in depth the characteristic of the artificial cochlea. We will report a novel piezoelectric
artificial cochlea which works on acoustic wave with frequency selectivity. This system is related
with the oscillation and piezoelectric effect. Experimentally, the frequency of the acoustic wave is
controlled from 1 kHz to 20 kHz, which covers the human audible frequency. The research paper
consists of an introduction as well as a brief description of the research objective, mechanical
model and design including results of the numerical simulation.
2. NUMERICAL SIMULATION
In this paper, we develop and simulate the model is based on the numerical method. The model is
developed and simulated based on the commercial CFD software, Fluent 6.3.26, and Gambit
2.4.6. The Fluent 6.3.26 use finite-volume code, generally this commercial software is used in
hydrodynamics and mass transfer computations [6]. The Gambit 2.4.6 is used to provide the
complete mesh flexibility, and solve the flow problems with both structured and unstructured
meshes. All functions of the Fluent and Gambit software’s are required to compute a solution and
display the results. These software are also accessible either through an interactive interface or
constructed using user-defined-functions (UDS).
The main challenge of the numerically predicting sound waves stems from the well-recognized
fact that sounds have much lower energy than fluid flows, typically by several orders of
magnitude. This poses a great challenge to the computation of sounds in terms of difficulty of
numerically resolving sound waves, especially when one is interested in predicting sound
propagation to the far field.
2.1. CFD
At present, Computational Fluid Dynamic (CFD) is a relatively new engineering tool that has
grown side by side with the development of the computer. CFD has been limited only by
computer RAM or lack of there, due to the use on billions upon billions of calculations and
iterations. Fluent was used in this project, this software was used to simulate flow in very small
duct (MEMS) and determine the pressure that occur in the duct with reasonable accuracy. This is
key to many fields of engineering because duct flows similar to this are used by the medical,
automotive, horticultural, and construction industries. As a result, equations and plots can be
created to assist industry in the production of better products.
It is the capability to develop larger and more complex simulations of real world fluid
experiments on computers. Generally, computers can be used to solve FEA stress simulations,
simulate fluid flow and heat transfer. Along with computers, the Navier-Stokes equation allowed
large, complex simulations of fluid flows to be solved. Equations below are the Navier-Stokes
equation [7].
3. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
27
Surprisingly, this equation was developed in the early nineteenth century by Claude Louis Marie
Navier and Sir George Gabriel Stokes. In CFD, the differential equations are used as algebraic
equations that describe small finite fluid elements that can then be estimated.
CFD simulation usually includes three steps: preprocessing, processing, and post processing. For
the preprocessing step, a specific system is identified. The geometry and material properties
should be clearly defined, and meshing usually follows after geometry is determined. This is
accomplished by dividing geometry into many small elements or volumes. Meshing in Gambit is
a complicated work, as it is critical for both the accuracy of final result and the cost of numerical
calculation. Generally, the finer the divided elements are the closer will be the final solution to
the true value at the expense of more calculation time. The setting of boundary conditions, initial
conditions and convergence criteria are also completed in the pre-processing stage. In the
processing stage, there are iterative calculations in each cell, which are carried out until
convergence criteria are met. This calculation-intensive process constitutes is the core content of
CFD application. It is not uncommon to have tens of thousands of calculation for even a
relatively simple problem. After the completion of processing, the results can be evaluated either
numerically or graphically. The graphical methods provide a more convenient way to evaluate the
overall effect. This includes signal plot, frequency, vector plot, plot of scalar variables, etc. These
visualization tools in post-processing stage allow quick assessment and comparison of calculation
results.
This research utilized a CFD program called Fluent. Fluent and CFD programs make guesses at
the solution and check how accurate the guess until it reaches a user defined “convergence.” The
convergence term is similar to an error value that results from the computers guess at the Navier-
Stokes equation and represents the precision of the simulation. Many of the Navier-Stokes
variables are input by the user such as the inlet pressures, boundaries, outlet types, and fluid
properties. Next, the computer makes a guess at the velocity field for the system and checks the
accuracy of its guess [8].
This cycle is referred to as a iteration. Iterations occur until the computers guesses reach the user
defined convergence point. Once that is accomplished, the user can examine the velocity field for
flow rates, pressures, energy transfers, and many other important characteristics. For this project,
the pressure into and out of the system was examined. However to use Fluent, a finite element
grid or mesh of the geometry was needed.
Packaged with Fluent is a program called Gambit. Gambit is a CAD (computer aided design)
program that allows a user to generate a model and then section it into small control volumes for
use in Fluent. The accuracy of the Fluent simulations are critically tied to this program because
Gambit is responsible for how many analysis points are placed in the model. Simply, put the
higher number of volumes, because the higher volumes can make more the accurate. This also
has a down side because with more volumes, comes longer iteration time. Moreover, this can
sometimes exceed the computers available memory and computational abilities.
This research explores the behavior of a fluid that is subjected to a force and the flow pattern that
is resulted. Basic mechanisms need to be understood before Fluent can be used because Fluent
requires the user to input the external constraints and set up the simulation environment. To
produce a fluid flow, a difference in pressure between the inlet and each outlet is required.
4. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
28
The governing equations of the sound waves through the duct are the mass continuity equation,
equilibrium equation and energy equation. Generally, the equation of motion governs fluid flow
is well known as the Navier-Stokes equation. Most researches on fluid dynamics are mostly
dedicated to get the solutions of this equation with particular boundary conditions, because of
difficulties in obtaining exact solutions for this kind of nonlinear equation [9].
The fluid characteristic by two parameters, fluid velocity and fluid density and the behavior of
fluid flow obeys two laws, for instance the conservation of mass and the conservation of
momentum. The conservation of mass means the initial and the final masses should remain the
same. Let consider a finite volume (∀) of fluid with A is a closed surface of the finite volume.
The mass of fluid using a finite volume is∫ ∀, the mass of fluid through a closed surface
is∫ ∀. The conservation of mass means that the incoming and outgoing flux of fluid are
conserved per unit time in a finite volume.
2.2. Two and Three Dimensional Model of PAM
The grid model of PAM with a basiliar membrane is shown in Figure 1. The PAM is one of
Microelectomechanical System (MEMS) device. In this study, the frequency selectivity of the
PAM can be investigated using computational fluid dynamics (CFD) and structural analysis.
Simulation model of the PAM consists of two cochlear ducts both filled with liquid. In the middle
of the ducts there is a basilar membrane. The membrane separates the ducts as shown in Figure 1.
The shape of basilar membrane is trapezoidal and the mechanical impedance changes along the
longitudinal direction. The acoustic pressure is applied to the liquid from the inlet region, the
membrane will be oscillated due to the fluid-structure interaction and it works as a so-called
variable wave guide. In order to clarify the characteristics of frequency selectivity, the fluid flow
and the vibration of the membrane are studied based on the numerical simulation.
We use the 2D and 3D models of the artificial cochlear in this calculation. The inlet position of
2D and 3D model are located at side of the membrane, respectively. The flow in the cochlear
ducts modeled as Newtonian, compressible, laminar and unsteady liquid flow based on the
Navier-Stokes equations. Surface of the membranes are treated as non-slip and rigid wall.
Equation of state relate the other variables to the two state variables, if we use ρ and T as state
variables we have state equations for pressure p and specific internal energy i, p = p (ρ , T) and i
= i (ρ , T), for perfect gas the following, equation of state are useful, p = ρ R T and i = cv T [6].
(1)
The compressible fluid flows, the equations of state provide the linkage between the energy
equation on the one hand, and mass conservation and momentum equations on the other. Without
density variation there is no linkage between the energy equation and the mass conservation and
momentum equations. The basic equations are numerically solved using Fluent 6.3.26.
Acoustic pressure pulse applied from the inlet to effectively analyze the response at various
frequencies. The width and the magnitude of the pulse define are 20 μs and 0.002 Pa,
respectively, where the pulse includes waves at all of the audible frequencies from 20 Hz to 20
kHz. The spatial distribution of acoustic pressure at a certain frequency obtained from the
frequency spectrum of the acoustic pressure.
gaugeopabs ppp +=
TR
pp
o
gaugeop
.
+
=
5. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
29
The meshing work for the geometry of the duct was done with Gambit 2.4.6 in 3-D analyses. Grid
refinement was performed according to the concentration gradient within the module geometry.
The final result represented the result after completing grid refinement and grid independence.
The pressure and momentum discretization schemes are second-order accurate, for time
integration process, first order implicit method used [14].
In this paper, we develop the model of artificial cochlea using plat bending theory and simulation
were based on the commercial CFD software, Fluent 6.3.26 and Gambit 2.4.6. The geometry of
the duct consist of 1 one-sided blocks of quadrilateral elements for 2D model and tetrahedral
elements for 3D model respectively, using map-type mesh and was exported to Fluent®
. In this
block, there are 31,903 nodes, 63,222 faces for 2D model and 188,796 nodes, 2,015,880 faces for
3D model. In this study we set the flow as compressible flow and we do not use the turbulence
flow because the exploration times are relatively short. Thus we consider the following
assumptions related to the fluid flow domain, the fluid is a continuous medium, Newtonian,
gravity is neglected and the flow is laminar. There are three positions of measurement, inlet,
middle and outlet regions as shown in Figure 1 (a) ~ (c).
(a) Trapezoidal Grid Design (b) Basal Grid (c) Apex Grid
Figure 1 Grid structure (a) Trapezoidal grid design (b) basal grid (c) apex grid
Figure 1 and 2 show the mesh and the grid of the duct model with the basilar membrane in the
middle and center of the cochlear duct.
(a) Part (b) Full
Figure 2. Mesh at the center of the duct model (a) part (b) full
The volume statistic for the modeling and simulations are shown,
6. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
30
Table 1. Volume statistic for modeling and simulations
Type Grid check 2D Model Grid check 3D Model
Domain Extents:
• x-coordinate
• y-coordinate
• z-coordinate
min (mm) =0.000e+000,
max (mm) = 30
min (mm) =0.000e+000,
max (mm) = 20
-
-
min (mm) = 0.000e+000,
max (mm) = 30.000e+000
min (mm) = 0.000e+000,
max (mm) = 20.000e+000
min (mm) = 0.000e+000,
max (mm) = 40.000e+000
Volume Statistic:
• min. volume
• max volume
2.448890e-007 (mm2)
7.943498e-003 (mm2)
1.571e-006 (mm3)
4.560e-004 (mm3)
Face area statistics :
• min face area
• max face area
2.000000e-005 (mm)
4.766194e-001 (mm)
8.319e-005 (mm2)
1.339e-002 (mm2)
Residual Criteria 1e-005 (continuity, velocity) and
1e-006 (Energy)
1e-005 (continuity, velocity) and
1e-006 (Energy)
Water-liquid is used in this simulation, this assumption based on the liquid fluid inside the
cochlea is endolymph that almost same with water characteristic. The detail information about
this fluid as shown in table below,
Material Properties,
Table 2. Characteristic of fluid : water-liquid (fluid)
Property Units Method Value(s)
Density kg/m3 user-defined (superfluid_density)
Cp (Specific Heat) j/kg-k constant 4182
Thermal Conductivity w/m-k constant 0.60000002
Viscosity kg/m-s constant 0.001003
Molecular Weight kg/kgmol constant 18.0152
Speed of Sound m/s user-defined (sound_speed)
Assumption in this simulation are,
• Fluid : Water – H20 (liquid)
• Laminar Flow
• Density : UDF (Tait Equation)
• Pressure Inlet : 0.002 Pa – for 20e-006 s
• Number of time step : 400
• BM model : Non-slip and Rigid wall
7. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
31
• Bulk Modulus (water) : 2.2E+09 Pa
• Density water (at 1 atm) : 1000 kg/m3
• Pressure Operating : 101325 Pa
• Temperature Operating : 300 K
The criteria for convergence were 1.0×10-5
for continuity, velocity and energy. The time step size
is determined to be 2.0 x 10-6
s and the total calculation time is 2.0 x 10-3
s.
• Input data :
• 10 time step with 0 Pa for 2.0 x 10-5
seconds
• 10 following time step using 0.002 Pa for 2.0 x 10-5
seconds
• 980 following time step using 0 Pa until 2.0 x 10-3
seconds
3. RESULTS AND DISCUSSION
In this paper, the liquid in the duct model is modeled as non-viscous fluid. The flow is a laminar
flow model which was controlled by pressure on the pressure-inlet option. The simulation models
are 2 and 3 dimensional square mesh with cartesian coordinate. The density of the cochlear fluids
that was filled into the duct is estimated using UDF. The simulation process is carried out using
unsteady time dependent calculation.
Figure 3 show the pressure distribution at inlet region of 2D model. In this figure the pressure was
given of 2.00E-05 seconds, and the simulation running for 8.00E-04 seconds.
(a) Upper (b) Lower
Figure 3. Pressure distribution at inlet point 2D model (a) upper (b) lower
Figure 4 show the pressure distribution at inlet region of 3D model. The pressure is gave with
same with 2Dmodel of 2.00E-05 seconds. Based on the Figure 3 and 2, the magnitude of the
pressure are explained that 2D model has bigger magnitude than 3D model, but generally the both
pressure distribution of 2D and 3D model has almost the similar forms.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
31
• Bulk Modulus (water) : 2.2E+09 Pa
• Density water (at 1 atm) : 1000 kg/m3
• Pressure Operating : 101325 Pa
• Temperature Operating : 300 K
The criteria for convergence were 1.0×10-5
for continuity, velocity and energy. The time step size
is determined to be 2.0 x 10-6
s and the total calculation time is 2.0 x 10-3
s.
• Input data :
• 10 time step with 0 Pa for 2.0 x 10-5
seconds
• 10 following time step using 0.002 Pa for 2.0 x 10-5
seconds
• 980 following time step using 0 Pa until 2.0 x 10-3
seconds
3. RESULTS AND DISCUSSION
In this paper, the liquid in the duct model is modeled as non-viscous fluid. The flow is a laminar
flow model which was controlled by pressure on the pressure-inlet option. The simulation models
are 2 and 3 dimensional square mesh with cartesian coordinate. The density of the cochlear fluids
that was filled into the duct is estimated using UDF. The simulation process is carried out using
unsteady time dependent calculation.
Figure 3 show the pressure distribution at inlet region of 2D model. In this figure the pressure was
given of 2.00E-05 seconds, and the simulation running for 8.00E-04 seconds.
(a) Upper (b) Lower
Figure 3. Pressure distribution at inlet point 2D model (a) upper (b) lower
Figure 4 show the pressure distribution at inlet region of 3D model. The pressure is gave with
same with 2Dmodel of 2.00E-05 seconds. Based on the Figure 3 and 2, the magnitude of the
pressure are explained that 2D model has bigger magnitude than 3D model, but generally the both
pressure distribution of 2D and 3D model has almost the similar forms.
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
31
• Bulk Modulus (water) : 2.2E+09 Pa
• Density water (at 1 atm) : 1000 kg/m3
• Pressure Operating : 101325 Pa
• Temperature Operating : 300 K
The criteria for convergence were 1.0×10-5
for continuity, velocity and energy. The time step size
is determined to be 2.0 x 10-6
s and the total calculation time is 2.0 x 10-3
s.
• Input data :
• 10 time step with 0 Pa for 2.0 x 10-5
seconds
• 10 following time step using 0.002 Pa for 2.0 x 10-5
seconds
• 980 following time step using 0 Pa until 2.0 x 10-3
seconds
3. RESULTS AND DISCUSSION
In this paper, the liquid in the duct model is modeled as non-viscous fluid. The flow is a laminar
flow model which was controlled by pressure on the pressure-inlet option. The simulation models
are 2 and 3 dimensional square mesh with cartesian coordinate. The density of the cochlear fluids
that was filled into the duct is estimated using UDF. The simulation process is carried out using
unsteady time dependent calculation.
Figure 3 show the pressure distribution at inlet region of 2D model. In this figure the pressure was
given of 2.00E-05 seconds, and the simulation running for 8.00E-04 seconds.
(a) Upper (b) Lower
Figure 3. Pressure distribution at inlet point 2D model (a) upper (b) lower
Figure 4 show the pressure distribution at inlet region of 3D model. The pressure is gave with
same with 2Dmodel of 2.00E-05 seconds. Based on the Figure 3 and 2, the magnitude of the
pressure are explained that 2D model has bigger magnitude than 3D model, but generally the both
pressure distribution of 2D and 3D model has almost the similar forms.
8. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
32
(a) Upper (b) Lower
Figure 4. Pressure distribution at inlet point 3D model (a) upper (b) lower
Figure 5 show the pressure distribution of 2D and 3D model at the center point of the membrane.
In the figure, magnitude of the pressure distribution using 3D model has lower than 2D model.
The wavelength of the 2D model is shorter than 3D model, so the frequency at the center point of
2D model is higher than 3D model at the same conditions.
(a) 2D Model (b) 3D Model
Figure 5. Pressure distribution at distance 14.5 mm (a) 2D and (b) 3D model
Figure 6 show the pressure distribution of 2D and 3D model at the end point of 29 mm from base
point. 2D model has more period than 3D, this indicate that analysis using 2D model has higher
frequency than 3D. It is caused by design of the model as dimension and amount of the grid
different each other. The both graph of 2D and 3D showed to the periodical in oscillation with the
value of the fluctuation that was not too big and the amplitude was tended to decrease
exponentially.
3D model2.5E-3
2.0E-3
1.5E-3
1.0E-3
5.0E-3
0.0E-3
-5.0E-3 Time (s)
0.0E0 1.0E-4 2.0E-4 3.0E-4 4.0E-4 5.0E-4 6.0E-4 7.0E-4 8.0E-4
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
32
(a) Upper (b) Lower
Figure 4. Pressure distribution at inlet point 3D model (a) upper (b) lower
Figure 5 show the pressure distribution of 2D and 3D model at the center point of the membrane.
In the figure, magnitude of the pressure distribution using 3D model has lower than 2D model.
The wavelength of the 2D model is shorter than 3D model, so the frequency at the center point of
2D model is higher than 3D model at the same conditions.
(a) 2D Model (b) 3D Model
Figure 5. Pressure distribution at distance 14.5 mm (a) 2D and (b) 3D model
Figure 6 show the pressure distribution of 2D and 3D model at the end point of 29 mm from base
point. 2D model has more period than 3D, this indicate that analysis using 2D model has higher
frequency than 3D. It is caused by design of the model as dimension and amount of the grid
different each other. The both graph of 2D and 3D showed to the periodical in oscillation with the
value of the fluctuation that was not too big and the amplitude was tended to decrease
exponentially.
3D model2.5E-3
2.0E-3
1.5E-3
1.0E-3
5.0E-3
0.0E-3
-5.0E-3 Time (s)
0.0E0 1.0E-4 2.0E-4 3.0E-4 4.0E-4 5.0E-4 6.0E-4 7.0E-4 8.0E-4
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
32
(a) Upper (b) Lower
Figure 4. Pressure distribution at inlet point 3D model (a) upper (b) lower
Figure 5 show the pressure distribution of 2D and 3D model at the center point of the membrane.
In the figure, magnitude of the pressure distribution using 3D model has lower than 2D model.
The wavelength of the 2D model is shorter than 3D model, so the frequency at the center point of
2D model is higher than 3D model at the same conditions.
(a) 2D Model (b) 3D Model
Figure 5. Pressure distribution at distance 14.5 mm (a) 2D and (b) 3D model
Figure 6 show the pressure distribution of 2D and 3D model at the end point of 29 mm from base
point. 2D model has more period than 3D, this indicate that analysis using 2D model has higher
frequency than 3D. It is caused by design of the model as dimension and amount of the grid
different each other. The both graph of 2D and 3D showed to the periodical in oscillation with the
value of the fluctuation that was not too big and the amplitude was tended to decrease
exponentially.
3D model2.5E-3
2.0E-3
1.5E-3
1.0E-3
5.0E-3
0.0E-3
-5.0E-3 Time (s)
0.0E0 1.0E-4 2.0E-4 3.0E-4 4.0E-4 5.0E-4 6.0E-4 7.0E-4 8.0E-4
9. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
33
(a) 2D Model (b) 3D Model
Figure 6. Pressure distribution at distance 29 mm of (a) 2D and (b) 3D model
Figure 7 show spectral density of 2D model at (a) inlet – upper (b) inlet – lower (c) center point
(d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point of the
membrane are occurred at the same frequency of 12.6 kHz. This indicates that the analysis
numerical of the membrane using 2D model has resonance frequency around 12.6 kHz.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 7. Spectral density of 2D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
33
(a) 2D Model (b) 3D Model
Figure 6. Pressure distribution at distance 29 mm of (a) 2D and (b) 3D model
Figure 7 show spectral density of 2D model at (a) inlet – upper (b) inlet – lower (c) center point
(d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point of the
membrane are occurred at the same frequency of 12.6 kHz. This indicates that the analysis
numerical of the membrane using 2D model has resonance frequency around 12.6 kHz.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 7. Spectral density of 2D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
33
(a) 2D Model (b) 3D Model
Figure 6. Pressure distribution at distance 29 mm of (a) 2D and (b) 3D model
Figure 7 show spectral density of 2D model at (a) inlet – upper (b) inlet – lower (c) center point
(d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point of the
membrane are occurred at the same frequency of 12.6 kHz. This indicates that the analysis
numerical of the membrane using 2D model has resonance frequency around 12.6 kHz.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 7. Spectral density of 2D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
10. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
34
Figure 8 show spectral density of 3D model at (a) inlet – upper (b) inlet – lower (c) center
point (d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point
of the membrane are occurred at the same frequency of 10.1 kHz. This indicates that the
analysis numerical of the membrane using 3D model has resonance frequency around
10.1 kHz during simulation of 8.00E-04 second.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 8. Spectral density of 3D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
4. CONCLUSIONS
It is considered that this phenomenon is caused by the local resonance frequency of the
membrane which varies along the longitudinal direction due to its trapezoidal shape. Based on the
numerical method developed here, the basic characteristic of the membrane is obtained. The
result shows that the frequency selectivity of the membrane is detected on the membrane surface.
This indicates that the analysis numerical of the membrane using 2D and 3D model is effective to
predict the frequency selectivity. The resonance frequencies are detected in the 2D and 3D model
of 12.6 kHz and 10.1 kHz, respectively. According to this result, the simulation program also can
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
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Figure 8 show spectral density of 3D model at (a) inlet – upper (b) inlet – lower (c) center
point (d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point
of the membrane are occurred at the same frequency of 10.1 kHz. This indicates that the
analysis numerical of the membrane using 3D model has resonance frequency around
10.1 kHz during simulation of 8.00E-04 second.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 8. Spectral density of 3D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
4. CONCLUSIONS
It is considered that this phenomenon is caused by the local resonance frequency of the
membrane which varies along the longitudinal direction due to its trapezoidal shape. Based on the
numerical method developed here, the basic characteristic of the membrane is obtained. The
result shows that the frequency selectivity of the membrane is detected on the membrane surface.
This indicates that the analysis numerical of the membrane using 2D and 3D model is effective to
predict the frequency selectivity. The resonance frequencies are detected in the 2D and 3D model
of 12.6 kHz and 10.1 kHz, respectively. According to this result, the simulation program also can
International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
34
Figure 8 show spectral density of 3D model at (a) inlet – upper (b) inlet – lower (c) center
point (d) end point. Frequency at the inlet-upper, inlet-lower, center point, and end point
of the membrane are occurred at the same frequency of 10.1 kHz. This indicates that the
analysis numerical of the membrane using 3D model has resonance frequency around
10.1 kHz during simulation of 8.00E-04 second.
(a) Inlet - Upper (b) Inlet - Lower
(c ) Center Point (d) End Point
Figure 8. Spectral density of 3D model (a) inlet – upper (b) inlet – lower (c) center point, and (d) end point
4. CONCLUSIONS
It is considered that this phenomenon is caused by the local resonance frequency of the
membrane which varies along the longitudinal direction due to its trapezoidal shape. Based on the
numerical method developed here, the basic characteristic of the membrane is obtained. The
result shows that the frequency selectivity of the membrane is detected on the membrane surface.
This indicates that the analysis numerical of the membrane using 2D and 3D model is effective to
predict the frequency selectivity. The resonance frequencies are detected in the 2D and 3D model
of 12.6 kHz and 10.1 kHz, respectively. According to this result, the simulation program also can
11. International Journal of Computer Science, Engineering and Applications (IJCSEA) Vol.2, No.4, August 2012
35
be applied to predict the pattern of pressure distribution and pressure changing on the membrane.
This prediction can be used for the next step using experimental method.
ACKNOWLEDGEMENTS
The author would like to acknowledge Kawano Laboratory members at Osaka University Japan
and the support of LPPI Tarumanagara University.
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Authors
Harto Tanujaya received S.T., M.T., and Ph.D. degrees in Mechanical Engineering
from Tarumanagara University (in 1996), University of Indonesia (in 1999) and
Osaka University (in 2010), respectively. He is a lecturer and researcher at
Tarumanagara University and his fields of research are includes artificial cochlea,
vibrations, and computational fluid dynamics (CFD). Recently, his current
research project is vibration of the artificial basilar membrane using specific
materials.