scholarly journals Simulation-Based Design and Optimization of Accelerometers Subject to High-Temperature and High-Impact Loads

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3759 ◽  
Author(s):  
Ji Li ◽  
Yaling Tian ◽  
Junjie Dan ◽  
Zhuming Bi ◽  
Jinhui Zheng ◽  
...  
Keyword(s):  
High Temperature ◽  
High Impact ◽  
Impact Loads ◽  
Sensing Element ◽  
Design And Optimization ◽  
Static And Dynamic Analysis ◽  
Simulation Based Design ◽  
Coupling Behavior ◽  
Simulation Based ◽  
Complete Failure

Due to multi-factor coupling behavior, the performance evaluation of an accelerometer subject to high-temperature and high-impact loads poses a significant challenge during its design phase. In this paper, the simulation-based method is applied to optimize the design of the accelerometer. The proposed method can reduce the uncertainties and improve the fidelity of the simulation in the sense that (i) the preloading conditions of fasteners are taken into consideration and modeled in static analysis; (ii) all types of loadings, including bolt preloads, thermal loads, and impact loads, are defined in virtual dynamic prototype of the accelerometer. It is our finding that from static and dynamic analysis, an accelerometer is exposed to the risk of malfunction and even a complete failure if the temperature rises to a certain limit; it has been proved that the thermal properties of sensing components are the most critical factors for an accelerometer to achieve its desired performance. Accordingly, we use a simulation-based method to optimize the thermal expansion coefficient of the sensing element and get the expected design objectives.

Simulation-based design and optimization of refrigeration cassettes

2011 ◽  
Vol 88 (12) ◽  
pp. 4756-4765 ◽  
Author(s):  
Maicon Waltrich ◽  
Christian J.L. Hermes ◽  
Cláudio Melo
Keyword(s):  
Design And Optimization ◽  
Simulation Based Design ◽  
Simulation Based

A Study of Covariance Functions for Multi-Response Metamodeling for Simulation-Based Design and Optimization

2008 ◽  
Author(s):  
David A. Romero ◽  
Cristina H. Amon ◽  
Susan Finger
Keyword(s):  
Performance Criteria ◽  
Test Cases ◽  
Data Sets ◽  
Cfd Analysis ◽  
System Behavior ◽  
Design And Optimization ◽  
Covariance Functions ◽  
Design Variables ◽  
Simulation Based Design ◽  
Simulation Based

The optimal design of complex systems in engineering requires the availability of mathematical models of system’s behavior as a function of a set of design variables; such models allow the designer to find the best solution to the design problem. However, system models (e.g. CFD analysis, physical prototypes) are usually time-consuming and expensive to evaluate, and thus unsuited for systematic use during design. Approximate models, or metamodels, of system behavior based on a limited set of data allow significant savings by reducing the resources devoted to modeling during the design process. In our work in engineering design based on multiple performance criteria, we propose the use of Multi-response Bayesian Surrogate Models (MRBSM) to model several aspects of system behavior jointly, instead of modeling each individually. By doing so, it is expected that the observed correlation among the response variables can be used to achieve better models with smaller data sets. In this work, we study the approximation capabilities of several covariance functions needed for multi-response metamodeling with MRBSM, performing a simulation study in which we compare MRBSM based on different covariance functions against metamodels built individually for each response. Our preliminary results indicate that MRBSM outperforms individual metamodels in 46% to 67% of the test cases, though the relative performance of the studied covariance functions is highly dependent on the sampling scheme used and the actual correlation among the observed response values.

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