Sentence examples for gas distribution model from inspiring English sources

This paper presents two approaches to gas distribution modelling, which introduce a time-dependency and a relation to a time-scale in generating the gas distribution model either by sub-sampling or by introducing a recency weight that relates measurement and prediction time.

For instance, remotely localizing a gas or odor source using mobile robot was proposed in [3] by fitting the gas distribution model to the gas sensor response at the sensor locations.

Most statistical gas distribution modelling methods assume that gas dispersion is a time-constant random process.

Next, we compared a time-dependent gas distribution modelling approach (TD Kernel DM+V), which includes a recency weight, to the state-of-the-art gas distribution modelling approach (Kernel DM+V), which does not consider sampling times.

While this assumption approximately holds in some situations, it is necessary to model variations over time in order to enable applications of gas distribution modelling in a wider range of realistic scenarios.

In this experiment we investigate gas distribution modelling, emission estimation and gas source localization with a mobile robot.

In collaboration with the researchers at Örebro University and working with different sensing principles, trainees develop novel algorithms for gas identification, gas distribution modelling and gas source localization.

The LDMvc is an improvement of the existing intersection-based LDM since the number of model tuning parameters is reduced to four through a sophisticated flow split algorithm, and a gas flow distribution model can be associated with the LDMvc.

A radial gas dispersion model was applied to analyse the gas hold-up distribution in the TPFB.

In the unconventional upstream oil and gas industry, spatial distribution modeling of rock mechanical properties of matured reservoir has always been a major challenge.

Key in this context is the ability to derive truthful models of gas distribution from a set of sparse measurements.