Different models for temperature and flow nonuniformities have been discussed.
These new numerical models for temperature forecasting trends provide an alternative solution to statistical systems based on climatological data analysis.
The non-iterative model is much faster and accurate in comparison to finite element simulation and recently presented iterative models for temperature computation.
The Classical Fourier law of heat conduction and consequent mathematical models for temperature dynamics constructed on the basis of parabolic partial differential equations assumes that the thermal disturbances propagate at infinite speeds.
Good overall agreement is observed between the experimental data and the DFGM and coupled UFGM models for temperature, particle size distribution, and OH concentration.
In this paper, a multiple model predictive control (MMPC) strategy based on Takagi Sugeno (T S) fuzzy models for temperature control of air-handling unit (AHU) in heating, ventilating, and air-conditioning (HVAC) systems is presented.
Part of the interest derives from the fact that future moisture conditions are much more difficult to project than future temperature, a fact that emerges when comparing the outputs of future GCMs for temperature and precipitation, where there is much higher agreement between models for temperature.
To most effectively control for the potential effects of weather conditions on mortality, we used distributed lag non-linear models for temperature, relative humidity, and air pressure.
Therefore, an obvious and important item for future work is to develop nonlinear regression models for temperature impacts on diarrhea; however, this work requires more accurate empirical data.
Naseri et al. (2005) presented a model for temperatures ranging from 40 to 146 °C.
