scholarly journals Artificial Intelligence-Based Optimization of a Bimorph-Segmented Tapered Piezoelectric MEMS Energy Harvester for Multimode Operation

Computation ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 84
Author(s):  
Osor Pertin ◽  
Koushik Guha ◽  
Olga Jakšić
Keyword(s):  
Genetic Algorithm ◽  
Goal Attainment ◽  
Low Frequency ◽  
Optimized Design ◽  
Vibration Energy Harvesting ◽  
Resonance Modes ◽  
Length Width ◽  
Artificial Neural Network Ann ◽  
Multimode Operation ◽  
Piezoelectric Harvester

This paper presents a study on the design and multiobjective optimization of a bimorph-segmented linearly tapered piezoelectric harvester for low-frequency and multimode vibration energy harvesting. The procedure starts with a significant number of FEM simulations of the structure with different geometric dimensions—length, width, and tapering ratio. The datasets train the artificial neural network (ANN) that provides the fitting function to be modified and used in algorithms for optimization, aiming to achieve minimal resonant frequency and maximal generated power. Levenberg–Marquardt (LM) and scaled conjugate gradient (SCG) methods were used to train the ANN, then the goal attainment method (GAM) and genetic algorithm (GA) were used for optimization. The dominant solution resulted from optimization by the genetic algorithm integrated with the ANN fitting function obtained by the SCG training method. The optimal piezoelectric harvester is 121.3 mm long and 71.56 mm wide and has a taper ratio of 0.7682. It ensures over five times greater output power at frequencies below 200 Hz, which benefits the low frequency of the vibration spectrum. The optimized design can harness the power of higher-resonance modes for multimode applications.

scholarly journals Magnetoelastic Coupling and Delta-E Effect in Magnetoelectric Torsion Mode Resonators

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2022
Author(s):  
Benjamin Spetzler ◽  
Elizaveta V. Golubeva ◽  
Ron-Marco Friedrich ◽  
Sebastian Zabel ◽  
Christine Kirchhof ◽  
...  
Keyword(s):  
Low Frequency ◽  
High Sensitivity ◽  
Element Model ◽  
Mode Number ◽  
Frequency Change ◽  
General Description ◽  
Resonance Modes ◽  
Field Sensor ◽  
Magnetic Sensitivity ◽  
Bending Modes

Magnetoelectric resonators have been studied for the detection of small amplitude and low frequency magnetic fields via the delta-E effect, mainly in fundamental bending or bulk resonance modes. Here, we present an experimental and theoretical investigation of magnetoelectric thin-film cantilevers that can be operated in bending modes (BMs) and torsion modes (TMs) as a magnetic field sensor. A magnetoelastic macrospin model is combined with an electromechanical finite element model and a general description of the delta-E effect of all stiffness tensor components Cij is derived. Simulations confirm quantitatively that the delta-E effect of the C66 component has the promising potential of significantly increasing the magnetic sensitivity and the maximum normalized frequency change ∆fr. However, the electrical excitation of TMs remains challenging and is found to significantly diminish the gain in sensitivity. Experiments reveal the dependency of the sensitivity and ∆fr of TMs on the mode number, which differs fundamentally from BMs and is well explained by our model. Because the contribution of C11 to the TMs increases with the mode number, the first-order TM yields the highest magnetic sensitivity. Overall, general insights are gained for the design of high-sensitivity delta-E effect sensors, as well as for frequency tunable devices based on the delta-E effect.

scholarly journals Experiment Investigation of Bistable Vibration Energy Harvesting with Random Wave Environment

2021 ◽  
Vol 11 (9) ◽  
pp. 3868
Author(s):  
Qiong Wu ◽  
Hairui Zhang ◽  
Jie Lian ◽  
Wei Zhao ◽  
Shijie Zhou ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Harvesting ◽  
Cantilever Beam ◽  
Large Scale ◽  
Low Frequency ◽  
Vibration Energy ◽  
Random Wave ◽  
Periodic Signal ◽  
Vibration Energy Harvesting ◽  
Motion Activity

The energy harvested from the renewable energy has been attracting a great potential as a source of electricity for many years; however, several challenges still exist limiting output performance, such as the package and low frequency of the wave. Here, this paper proposed a bistable vibration system for harvesting low-frequency renewable energy, the bistable vibration model consisting of an inverted cantilever beam with a mass block at the tip in a random wave environment and also develop a vibration energy harvesting system with a piezoelectric element attached to the surface of a cantilever beam. The experiment was carried out by simulating the random wave environment using the experimental equipment. The experiment result showed a mass block’s response vibration was indeed changed from a single stable vibration to a bistable oscillation when a random wave signal and a periodic signal were co-excited. It was shown that stochastic resonance phenomena can be activated reliably using the proposed bistable motion system, and, correspondingly, large-scale bistable responses can be generated to realize effective amplitude enlargement after input signals are received. Furthermore, as an important design factor, the influence of periodic excitation signals on the large-scale bistable motion activity was carefully discussed, and a solid foundation was laid for further practical energy harvesting applications.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Genetic algorithm- and finite element-based design and analysis of nonprismatic piezolaminated beam for optimal vibration energy harvesting

2015 ◽  
Vol 230 (14) ◽  
pp. 2532-2548 ◽  
Author(s):  
Alok Ranjan Biswal ◽  
Tarapada Roy ◽  
Rabindra Kumar Behera
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Energy Harvesting ◽  
Output Power ◽  
Governing Equation ◽  
Optimization Technique ◽  
Vibration Energy ◽  
Fe Model ◽  
Vibration Energy Harvesting ◽  
Real Coded Genetic Algorithm

The current article deals with finite element (FE)- and genetic algorithm (GA)-based vibration energy harvesting from a tapered piezolaminated cantilever beam. Euler–Bernoulli beam theory is used for modeling the various cross sections of the beam. The governing equation of motion is derived by using the Hamilton's principle. Two noded beam elements with two degrees of freedom at each node have been considered in order to solve the governing equation. The effect of structural damping has also been incorporated in the FE model. An electric interface is assumed to be connected to measure the voltage and output power in piezoelectric patch due to charge accumulation caused by vibration. The effects of taper (both in the width and height directions) on output power for three cases of shape variation (such as linear, parabolic and cubic) along with frequency and voltage are analyzed. A real-coded genetic algorithm-based constrained (such as ultimate stress and breakdown voltage) optimization technique has been formulated to determine the best possible design variables for optimal harvesting power. A comparative study is also carried out for output power by varying the cross section of the beam, and genetic algorithm-based optimization scheme shows the better results than that of available conventional trial and error methods.

Design Optimization of Low Power and Low Frequency Vibration Based MEMS Energy Harvester

2013 ◽  
Vol 475-476 ◽  
pp. 1624-1628
Author(s):  
Hasnizah Aris ◽  
David Fitrio ◽  
Jack Singh
Keyword(s):  
Low Power ◽  
Energy Harvester ◽  
Piezoelectric Materials ◽  
Geometry Optimization ◽  
Low Frequency ◽  
Substrate Thickness ◽  
Power Harvesting ◽  
Low Frequency Vibration ◽  
Length Width ◽  
Development And Utilization

The development and utilization of different structural materials, optimization of the cantilever geometry and power harvesting circuit are the most commonly methods used to increase the power density of MEMS energy harvester. This paper discusses the cantilever geometry optimization process of low power and low frequency of bimorph MEMS energy harvester. Three piezoelectric materials, ZnO, AlN and PZT are deposited on top and bottom of the cantilever Si substrate. This study focuses on the optimization of the cantilevers length, width, substrate thickness and PZe thickness in order to achieve lower than 600 Hz of resonant frequency. The harvested power for this work is in the range of 0.02 ~ 194.49 nW.

scholarly journals Modeling, Simulation and Optimization of Piezoelectric Bimorph Transducer for Broadband Vibration Energy Harvesting

2017 ◽  
Vol 6 (4) ◽  
pp. 5 ◽  
Author(s):  
Nan Chen ◽  
Vishwas Bedekar
Keyword(s):  
Energy Harvester ◽  
Piezoelectric Properties ◽  
Vibration Energy ◽  
Piezoelectric Bimorph ◽  
Vibration Energy Harvesting ◽  
Compliance Matrix ◽  
Simulation And Optimization ◽  
Power Characteristics ◽  
Modeling Simulation ◽  
Length Width

We demonstrate the detailed analysis for conversion of piezoelectric properties into compliance matrix and simulate a series bimorph configuration for vibration based energy generation. Commercially available software COMSOL Multiphysics was used to apply boundary conditions for optimization of geometric parameters such as length, width and thickness of piezoelectric layer to study voltage and power characteristics of the harvester. The resulting energy harvester was found to generate 1.73 mW at 53.4 Hz across a 3MW load with an energy density of 13.08mJ/cm3. We also investigated feasibility of this model by comparing it with existing experimental data of known piezoelectric ceramic compositions and found good correlation between the two.

scholarly journals Statistical and Machine Learning-Driven Optimization of Mechanical Properties in Designing Durable HDPE Nanobiocomposites

Polymers ◽  
2021 ◽  
Vol 13 (18) ◽  
pp. 3100
Author(s):  
Anusha Mairpady ◽  
Abdel-Hamid I. Mourad ◽  
Mohammad Sayem Mozumder
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Genetic Algorithm ◽  
Machine Learning Techniques ◽  
Hybrid Technique ◽  
Learning Techniques ◽  
Artificial Neural Network Ann ◽  
Major Factors ◽  
The Cost ◽  
Design Factors

The selection of nanofillers and compatibilizing agents, and their size and concentration, are always considered to be crucial in the design of durable nanobiocomposites with maximized mechanical properties (i.e., fracture strength (FS), yield strength (YS), Young’s modulus (YM), etc). Therefore, the statistical optimization of the key design factors has become extremely important to minimize the experimental runs and the cost involved. In this study, both statistical (i.e., analysis of variance (ANOVA) and response surface methodology (RSM)) and machine learning techniques (i.e., artificial intelligence-based techniques (i.e., artificial neural network (ANN) and genetic algorithm (GA)) were used to optimize the concentrations of nanofillers and compatibilizing agents of the injection-molded HDPE nanocomposites. Initially, through ANOVA, the concentrations of TiO2 and cellulose nanocrystals (CNCs) and their combinations were found to be the major factors in improving the durability of the HDPE nanocomposites. Further, the data were modeled and predicted using RSM, ANN, and their combination with a genetic algorithm (i.e., RSM-GA and ANN-GA). Later, to minimize the risk of local optimization, an ANN-GA hybrid technique was implemented in this study to optimize multiple responses, to develop the nonlinear relationship between the factors (i.e., the concentration of TiO2 and CNCs) and responses (i.e., FS, YS, and YM), with minimum error and with regression values above 95%.

scholarly journals Optimized design of concrete-filled steel columns

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jéssica Salomão Lourenção ◽  
Paulo Augusto Tonini Arpini ◽  
Gabriel Erlacher ◽  
Élcio Cassimiro Alves
Keyword(s):  
Genetic Algorithm ◽  
Cost Efficiency ◽  
Optimization Problem ◽  
Optimized Design ◽  
Composite Columns ◽  
Steel Columns ◽  
Normal Forces ◽  
Steel Bars ◽  
Genetic Algorithm Method ◽  
Filled Composite

Abstract The objective of this paper is to present the formulation of the optimization problem and its application to the design of concrete-filled composite columns with and without reinforcement steel bars, according to recommendations from NBR 8800:2008, NBR 16239:2013 and EN 1994-1-1:2004. A comparative analysis between the aforementioned standards is performed for various geometries considering cost, efficiency and materials in order to verify which parameters influence the solution of the composite column that satisfies the proposed problems. The solution of the optimization problem is obtained by using the genetic algorithm method featured in MATLAB’s guide toolbox. For the examples analyzed, results show that concretes with compressive strength greater than 50MPa directly influence the solution of the problem regarding cost and resistance to normal forces.

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