Numerical prediction of steady state temperature based on transient measurements

Abstract

We show how to use numerical analysis of short-time range experimental data for predicting the limit steady-state value of the investigated parameter. In this article the approach has been applied to a specific, although typical, thermal problem: determining the average steady-state temperature of a heater in the convective and radiative heat exchange with the environment. First, we describe a heat exchange experiment aimed at obtaining temperature experimental data in both short and long time range. Then we present a methodology for applying two methods, i.e., neural networks and least squares approximations, for obtaining predictions about the steady-state temperature values based on short time experimental data. The aim of the study is to compare the predictions to each other and to the long time experimental values, with the aim of determining the applicability range of the two methods.

Emissivity measurement of black paint using the calorimetric method

“…1, was originally built to assess the correctness of neural networks for predicting the temperature of heated solids in the distant future, which in practice meant determining the steadystates temperature at specific points on the body. The photograph with main components of the rig can be found in the article [32]. It has been adapted to determine the relative emissivity of black paint, mentioned in the title of the work and allows a proper thermal equilibrium to be achieved.…”

Прогнозування Попиту На Пасажирські Перевезення Таксі Методами Нейронної Мережі

“…Відомо [38], що попит на пасажирські перевезення таксі може мінятися під впливом різних чинників, а часто проблеми виникають й через відсутність місць паркування. Нерідко у певних районах, особливо ближче до центральної частини міста, не вистачає вільних місць на парковці.…”