Interval process model and non-random vibration analysis

Han

Numer Methods Biomed Eng

et al. 2017

Dynamic load exists in numerous biomechanical systems, and its identification signifies a critical issue for characterizing dynamic behaviors and studying biomechanical consequence of the systems. This study aims to identify dynamic load in the dental prosthetic structures, namely, 3-unit implant-supported fixed partial denture (I-FPD) and teeth-supported fixed partial denture. The 3-dimensional finite element models were constructed through specific patient's computerized tomography images. A forward algorithm and regularization technique were developed for identifying dynamic load. To verify the effectiveness of the identification method proposed, the I-FPD and teeth-supported fixed partial denture structures were investigated to determine the dynamic loads. For validating the results of inverse identification, an experimental force-measuring system was developed by using a 3-dimensional piezoelectric transducer to measure the dynamic load in the I-FPD structure in vivo. The computationally identified loads were presented with different noise levels to determine their influence on the identification accuracy. The errors between the measured load and identified counterpart were calculated for evaluating the practical applicability of the proposed procedure in biomechanical engineering. This study is expected to serve as a demonstrative role in identifying dynamic loading in biomedical systems, where a direct in vivo measurement may be rather demanding in some areas of interest clinically.

({,, \begin{array}{l} boldM \overset{u}{true} ((),, t) + boldC \overset{u}{true} ((),, t) + boldKu ((),, t) = boldf ((),, t) \\ boldu ((),, 0) = u^{0}, \overset{u}{true} ((),, 0) = {\overset{falsė}{boldu}}^{0} \end{array})

where u ( t ),

\overset{u}{true}

( t ), and

\overset{u}{true}

( t ) are the vectors of time‐variant displacement, velocities, and accelerations, respectively; and f ( t ) is the time‐dependent vector of the external dynamic excitation forces; u 0 and

{\overset{falsė}{boldu}}^{0}

denote the initial displacement and velocity conditions.

boldu ((),, t) = ([],, \begin{array}{l} u_{1} ((),, t) \\ u_{2} ((),, t) \\ ⋮ \\ u_{n} ((),, t) \end{array}), \overset{u}{true} ((),, t) = ([],, \begin{array}{l} {\overset{falsė}{boldu}}_{1} ((),, t) \\ {\overset{falsė}{boldu}}_{2} ((),, t) \\ ⋮ \\ {\overset{falsė}{boldu}}_{n} ((),, t) \end{array}), \overset{u}{true} ((),, t) = ([],, \begin{array}{l} {\overset{false¨}{boldu}}_{1} ((),, t) \\ {\overset{false¨}{boldu}}_{2} ((),, t) \\ ⋮ \\ {\overset{false¨}{boldu}}_{n} ((),, t) \end{array}), boldf ((),, t) =...

…”

Section: Methodsmentioning

confidence: 99%

Identification of dynamic load for prosthetic structures

Han

Numer Methods Biomed Eng

et al. 2017

Engineering Optimization

“…in which the mass matrix M and the stiffness matrix K are the same as those in Eq. (15), and the damping matrix  C can be expressed as…”

Section: Stability Analysis Of the Driveline Juddermentioning

confidence: 99%

“…There are many uncertain models to describe the uncertainty parameters, such as probability model [14], interval model [15] and convex model [16]. Different uncertain models are used to describe different kinds of parameters.…”

Section: Introductionmentioning

confidence: 99%

Analysis and optimization of clutch judder based on a hybrid uncertain model with random and interval variables

Hao

et al. 2018

The clutch judder has serious impacts on the NVH (noise, vibration and harshness) performance. In this paper, a simplified four-degree-of-freedom dynamic model with nonlinear friction torque and engine torque excitation is developed to simulate the clutch judder, and the stability and dynamic response of the clutch is analyzed based on the simplified model. In addition, the real part of judder modal frequency, the moment when the clutch enters the stick state, and the fluctuation level of the driving part of clutch are treated as the evaluation indexes of the judder performance. An uncertain hybrid model with random and interval variables is used to described the uncertainty of parameters and a hybrid perturbation vertex method is formulated to compute the uncertainty of the clutch judder. Furthermore, the parameters with high sensitivities are used as design variables and the uncertainty-based optimization are conducted to reduce the clutch judder.The optimization results have strongly validated that the proposed method is very effective to improve the robustness of the clutch judder performance.

“…} is called an interval process model, denoted as X I (t) [23]. For the interval process model X I (t), X U (t), and X L (t) are the upper and lower bound functions, respectively.…”

Section: Interval Process Modelmentioning

confidence: 99%

Evaluation of Wind‐Induced Response Bounds of High‐Rise Buildings Based on a Nonrandom Interval Analysis Method

Wei

Zhao

2018

Shock and Vibration

The traditional wind-induced response analysis of high-rise buildings conventionally considers the wind load as a stationary stochastic process. That is, for a certain wind direction angle, the reference wind speed (usually refers to the mean wind speed at the building height) is assumed to be a constant corresponding to a certain return period. Combined with the recorded data in wind tunnel test, the structural response can be computed using the random vibration theory. However, in the actual typhoon process, the average wind speed is usually time-variant. This paper combines the interval process model and the nonrandom vibration analysis method with the wind tunnel test and proposes a method for estimating the response boundary of the high-rise buildings under nonstationary wind loads. With the given upper and lower bounds of time-variant wind excitation, this method can provide an effective calculation tool for estimating wind-induced vibration bounds for high-rise buildings under nonstationary wind load. The Guangzhou East tower, which is 530 m high and the highest supertall building in Guangzhou, China, was taken as an example to show the effectiveness of the method. The obtained boundary response can help disaster prevention and control during the passage of typhoons.