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Micro-Electro-Mechanical System (MEMS)-based pressure sensors operating on the principle of piezoresistivity have found profound application in various fields like automobile, aerospace, aviation, biomedical and consumer electronics. Various research studies have been conducted to optimize the design of MEMS-based pressure sensors to meet specific requirements of different fields. Modification in the structure of the piezoresistors placed on these sensors has shown great effect in this regard. However, most of these improvements have been validated through fabrication and measurement, but there has been a lack of significant studies developing analytical models to explain these improvements. This paper studies the performance of a single-turn piezoresistor design on a square silicon diaphragm. The analytical model relates the dimensions of the single-turn piezoresistor on a square diaphragm to the output voltage, and hence, sensor sensitivity is laid out. The correctness of the relation is also validated through Finite Element Analysis (FEA) performed using COMSOL Multiphysics software. Hence, an optimized single-turn design is presented which achieves a sensitivity of 203.57 mV/V/MPa over a pressure range of 0–1 MPa. These results are then compared to work from existing literature. The comparison shows an improved performance which was achieved by optimizing the design through its derived analytical model. The proposed sensor can be utilized in disposable blood pressure measurement system where high sensor sensitivity is required. [ABSTRACT FROM AUTHOR]

The paper presents the results of work leading to the construction of a spatial hybrid model based on finite element (FE) and Monte Carlo (MC) methods allowing the computer simulation of physical phenomena accompanying the steel sample testing at temperatures that are characteristic for soft-reduction process. The proposed solution includes local density variations at the level of mechanical solution (the incompressibility condition was replaced with the condition of mass conservation), and at the same time simulates the grain growth in a comprehensive resistance heating process combined with a local remelting followed by free/controlled cooling of the sample tested. Simulation of grain growth in the entire computing domain would not be possible without the support of GPU processors. There was a 59-fold increase in the computing speed on the GPU compared to single-threaded computing on the CPU. The study was complemented by examples of experimental and computer simulation results, showing the correctness of the adopted model assumptions. [ABSTRACT FROM AUTHOR]

In this paper, we present a novel 1D singularly perturbed reaction–convection–diffusion mathematical model, with non-linear coefficients (SP-RCD model), for the physical modeling of a fuel cell. The model is a generalization of the macro-homogeneous model, revisited from the point of view of singularly perturbed differential equations. To solve the system of coupled second-order differential equations, we propose a numerical scheme based on vanishing the artificial diffusion of the finite element method within an iterative fixed-point algorithm. We also propose an adaptive Shishkin mesh, as a function of the derivative of the current density in the subdomain with a fast-growing slope. Results of the proposed SP-RCD model are comparable to those of the macro-homogeneous model. In addition, it describes the oxygen concentration profiles in the thickness of the cathode catalytic layer under different operating currents and represents, with enough precision, the experimental polarization curve reported in the literature. • We introduce a novel RCD mathematical model for the numerical simulation of PEM fuel cells. • We introduce a method for stabilizing the numerical solution, it introduces artificial diffusion (AD), until stabilization, then AD is removed. • We introduce a numerical strategy for adapting a Shishkin mesh, avoiding user-made tuning. • Mathematical arguments about convergence are provided. • We validate the algorithm with reported cases and other model. [ABSTRACT FROM AUTHOR]