Degree Of Freedom Analytical Model Research Articles

Effect of damage and support damping mechanisms on unimorph piezoelectric energy harvester

Damping plays a critical role in power generation by piezoelectric energy harvesting, and yet there is a lack of sensitivity studies on different sources of damping. In this paper, two damping sources in unimorph piezoelectric energy harvesters, namely support loss and damage damping mechanisms, are experimentally investigated. Variations of the power generation are evaluated with respect to the sources of damping. Accordingly, the power generation model is developed according to the experimental results in this work and using a single degree of freedom analytical model. This study focuses on the debonding effect, as an internal damping source, and support loss, as a critical source of external energy dissipation. The results show that the debonding reduces the output power dramatically at resonance and, particularly, at anti-resonance frequencies. Moreover, investigation of the support loss shows that the material of clamp as well as installation torque have an impact on the support loss and, consequently, affect the output power.

FWD Halfshaft Angle Optimization Using 12 Degree of Freedom Analytical Model

FWD Halfshaft Angle Optimization Using 12 Degree of Freedom Analytical Model

A MEMS Piezoelectric Cantilever Beam Array for Vibration Energy Harvesting

A micro piezoelectric cantilever beam array is designed for vibration energy harvesting. A single degree of freedom analytical model is developed to predict the properties of the device and is verified by finite element method. The piezoelectric material Aluminum Nitride was chosen for the compatibility with the CMOS process. The devices consisting of 5 piezoelectric cantilever beams and one proof mass were fabricated using micromachining technology. The resonance frequency, voltage and power were tested at excitation acceleration of 5.0 g. The maximum output power of the device is 9.13 μW at the resonance frequency of 1315 Hz when piezoelectric beams are connected in parallel.

Nano- and micro-electromechanical switch dynamics

This paper reports theoretical analysis and experimental results on the dynamics of piezoelectric MEMS mechanical logic relays. The multiple degree of freedom analytical model, based on modal decomposition, utilizes modal parameters obtained from finite element analysis and an analytical model of piezoelectric actuation. The model accounts for exact device geometry, damping, drive waveform variables, and high electric field piezoelectric nonlinearity. The piezoelectrically excited modal force is calculated directly and provides insight into design optimization for switching speed. The model accurately predicts the propagation delay dependence on actuation voltage of mechanically distinct relay designs. The model explains the observed discrepancies in switching speed of these devices relative to single degree of freedom switching speed models and suggests the strong potential for improved switching speed performance in relays designed for mechanical logic and RF circuits through the exploitation of higher order vibrational modes.

Predicting Changes in Vibration Behavior With Respect to Multiple Variables Using Empirical Sensitivity Functions

Engineers must routinely predict how structural systems will vibrate after design modifications are made to the mass, damping, or stiffness properties of the components. To reduce the cost of product development, sensitivity prediction methods are desired that can be applied using only empirical data from an initial prototype. Embedded sensitivity functions derived solely from empirical data have previously been applied (a) to identify optimal design modifications for reducing linear vibration resonance problems and (b) to predict the change in frequency response. In this previous work, predictive methods were developed that assumed that only one design parameter in the system was modified. In many applications, it is necessary to extend this approach to all major parameters for a more accurate prediction of the structural dynamic response. This paper utilizes a multivariable Taylor series to take into account multiple parameter changes that affect a broadband frequency range. The method is applied to a single degree of freedom analytical model to determine the accuracy of the predictions. Finite element analyses are then conducted on a three-story structure and an automotive vehicle component with modifications to the stiffness and mass distributions to demonstrate the feasibility of these predictions in applications to more complicated structural systems.

Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy

We have developed a long-term cardiorespiratory sensor system that includes a wearable sensor probe with adaptive hardware filters and data processing algorithms ( Choi & Jiang, 2006, 2008). However, the data processing algorithm proposed for the R–R interval (RRI) information extraction did not work well in the case of ECG signals with baseline shifts or muscle artifacts. Furthermore, many false ECG beats were extracted due to a weak decision-making scheme. Then, those false beats produced irregular RRI information and erroneous heart rate variability results. Modification of data processing algorithm was strongly needed. Therefore, this work presented an efficient ECG beat segmentation method using an irregular RRI checkup strategy into five sequential RRI patterns. This algorithm was comprised of signal processing stage and ECG beat detector stage. The signal processing included the wavelet denoising, the baseline shift elimination by 20 Hz lowpass filter and the envelope curve extraction by a single degree of freedom analytical model. The ECG beat detector included the candidate ECG beat detection and segmentation by one threshold and by irregular RRI checkup strategy, respectively. In particular, four abnormal RRI patterns were proposed to find out false ECG beats. The MIT-BIH arrhythmia database was selected as the dataset for testing the proposed algorithm. The proposed irregular RRI checkup strategy estimated 5463 beats to the suspected false beats and succeeded in segmenting 96.19% (5255 beats) of them. The performance results showed that our algorithm had very good results such as the detection error of 0.54%, sensitivity of 99.66% and positive predictivity of 99.80%. Furthermore, our algorithm showed very high accuracy as the mean time error between the beat annotations of the database and our obtained beat occurence times was 7.75 ms.

Predicting changes in vibration behavior using first- and second-order iterative embedded sensitivity functions

In product manufacturing and test environments, engineers must predict how mechanical components will vibrate when design modifications are made to the mass, damping, or stiffness properties of the components. If component models are not available, then engineers must rely on test data from the initial component to conduct their design sensitivity analysis. Embedded sensitivity functions derived solely from test data have previously been applied to identify optimal design modifications for reducing linear vibration resonance problems in certain frequency ranges. However, forced response predictions were not accurate due to the nonlinear nature of the frequency response function variation for large design modifications. This paper develops two techniques for predicting the forced response of mechanical components for local changes in properties based on (a) first-order multi-step iterative prediction and (b) second-order iterative sensitivity functions. The methods are applied to a single degree of freedom analytical model to determine the accuracy of the predictions. Some tests and finite element analyses are conducted on a cantilever beam, a sub-system of an automotive vehicle, a structural component of a truck dumping system, and a lever arm with a modification to the mass distribution to demonstrate the feasibility of these predictions in experimental and analytical applications.

An investigation of hoist-induced dynamic loads on bridge crane structures

A series of tests were performed on a bridge crane to investigate how the peak dynamic response during hoisting is affected by the stiffness of the crane structure, the inertial properties of the crane structure, the stiffness of the cable-sling system, the payload mass, and the initial conditions for the hoisting operation. These factors were varied in the test program and time histories were obtained for displacements, accelerations, cable tension, bridge bending moment, and end truck wheel reactions. Values for the dynamic ratio, defined as peak dynamic value over corresponding static value, are presented for displacements, bridge bending moment, and end truck wheel reactions. A two degree of freedom analytical model is presented, and theoretical values for the dynamic ratio are calculated as a function of three dimensionless parameters that characterize the crane and payload system. The predicted dynamic ratios are found to be conservative when compared with the test results. A general format is suggested for dynamic factors in design standards that apply to bridge cranes with constant speed motors. Key words: bridge crane, hoist, dynamic load.