Topology optimization enhances the distinguishability and reconstructability of electrical resistance tomography based sensors

TL;DR

This paper uses topology optimization to design background conductivities in electrical resistance tomography sensors, aiming to improve their distinguishability and reconstructability, thereby enhancing sensor performance.

Contribution

It introduces a novel approach of applying topology optimization to engineer background conductivity for better ERT sensor distinguishability and reconstructability.

Findings

Topology optimization effectively enhances sensor distinguishability.

Optimized sensors show improved reconstructability in ERT.

The method provides a new design paradigm for ERT sensors.

Abstract

In the majority of applications of electrical resistance tomography (ERT) the estimation problem consists of either the estimation of spatial conductivity change over an existing background or the estimation of spatial distribution of conductivity of the entire target, including the background. In some instances however, it is possible to design the background conductivity; an example of such application is the design of ERT-based sensors where the background conductivity can be engineered. In such applications the natural question is whether the background conductivity can be engineered in such a way to increase the distinguishability and further reconstructability of the sensor. The present paper, uses topology optimization to design the background conductivity to achieve optimal distinguishability. Then, ERT reconstructions suggest the enhancements of reconstructability using topology optimized sensor.