Thermal Accelerometer Simulation by the R‑Functions Method

Басараб, М. А.; Giani, Alain; Combette, Philippe

doi:10.3390/app10238373

Cited by 6 publications

(3 citation statements)

References 29 publications

(43 reference statements)

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“…On the other hand, precise parameter identification which describes the relationship between TCQs and TDE is another key factor, and it requires the prerequisites that the TDE estimation model is accurate. An accurate TDE estimation model focuses on the relationship between its inputs and outputs, and the bias stability of CMGs is compensated to the target ones with its inputs [ 26 ]. Cheng et al apply a particle swarm optimization algorithm in optimizing support vector machine models to improve bias stability.…”

Section: Introductionmentioning

confidence: 99%

A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis

Qi,

Cheng,

Wang

et al. 2024

Micromachines

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Because the conventional Temperature Drift Error (TDE) estimation model for Capacitive MEMS Gyros (CMGs) has inadequate Temperature Correlated Quantities (TCQs) and inaccurate parameter identification to improve their bias stability, its novel model based on thermal stress deformation analysis is presented. Firstly, the TDE of the CMG is traced precisely by analyzing its structural deformation under thermal stress, and more key decisive TCQs are explored, including ambient temperature variation ∆T and its square ∆T2, as well its square root ∆T1/2; then, a novel TDE estimation model is established. Secondly, a Radial Basis Function Neural Network (RBFNN) is applied to identify its parameter accurately, which eliminates local optimums of the conventional model based on a Back-Propagation Neural Network (BPNN) to improve bias stability. By analyzing heat conduction between CMGs and the thermal chamber with heat flux analysis, proper temperature control intervals and reasonable temperature control periods are obtained to form a TDE precise test method to avoid time-consuming and expensive experiments. The novel model is implemented with an adequate TCQ and RBFNN, and the Mean Square Deviation (MSD) is introduced to evaluate its performance. Finally, the conventional model and novel model are compared with bias stability. Compared with the conventional model, the novel one improves CMG’s bias stability by 15% evenly. It estimates TDE more precisely to decouple Si-based materials’ temperature dependence effectively, and CMG’s environmental adaptability is enhanced to widen its application under complex conditions.

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Section: Introductionmentioning

confidence: 99%

A Novel Temperature Drift Error Estimation Model for Capacitive MEMS Gyros Using Thermal Stress Deformation Analysis

Qi,

Cheng,

Wang

et al. 2024

Micromachines

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show abstract

“…After obtaining accurate TDE traceability, describing the relationship between TCQ and TDE precisely is another key factor, and the TDE estimation model is accurate and efficient. Establishing the TDE estimation model accurately focuses on the relationship between its input and output by algorithm, and the outputs of MEMS accelerometers are compensated to the desired ones with its inputs [20]. Reference [21] proposes a modified Support Vector Machine (SVM) model with a structural risk minimization principle, and PSO is introduced in optimizing SVM and increasing the MEMS accelerometer's output accuracy.…”

Section: Introductionmentioning

confidence: 99%

A Novel Temperature Drift Error Precise Estimation Model for MEMS Accelerometers Using Microstructure Thermal Analysis

Shi

Zhang

et al. 2022

Micromachines

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Owing to the fact that the conventional Temperature Drift Error (TDE) precise estimation model for a MEMS accelerometer has incomplete Temperature-Correlated Quantities (TCQ) and inaccurate parameter identification to reduce its accuracy and real time, a novel TDE precise estimation model using microstructure thermal analysis is studied. First, TDE is traced precisely by analyzing the MEMS accelerometer’s structural thermal deformation to obtain complete TCQ, ambient temperature T and its square T2, ambient temperature variation ∆T and its square ∆T2, which builds a novel TDE precise estimation model. Second, a Back Propagation Neural Network (BPNN) based on Particle Swarm Optimization plus Genetic Algorithm (PSO-GA-BPNN) is introduced in its accurate parameter identification to avoid the local optimums of the conventional model based on BPNN and enhance its accuracy and real time. Then, the TDE test method is formed by analyzing heat conduction process between MEMS accelerometers and a thermal chamber, and a temperature experiment is designed. The novel model is implemented with TCQ and PSO-GA-BPNN, and its performance is evaluated by Mean Square Error (MSE). At last, the conventional and novel models are compared. Compared with the conventional model, the novel one’s accuracy is improved by 16.01% and its iterations are reduced by 99.86% at maximum. This illustrates that the novel model estimates the TDE of a MEMS accelerometer more precisely to decouple temperature dependence of Si-based material effectively, which enhances its environmental adaptability and expands its application in diverse complex conditions.

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“…While wall shear stress is an important parameter in the analysis of aneurysm flow diversion effects, it was not calculated in this study due to light reflection at the lumen wall that may lead to error in the cross-correlation algorithm, whereas CFD analysis will be implemented soon in three dimensions and high resolution. Finally, the third paper, authored by Basarab, M. et al [6], presents a novel approach for solving CFD problems in the thermal accelerometer's cavity, which is based on the combined use of Rvachev's R-functions method and the Galerkin technique. Different bases were applied in this work, both spectral (polynomial) and local (B-splines), and good results were achieved for fields evaluated in domains of simple geometry without localized inhomogeneities.…”

mentioning

confidence: 99%