For each experience, a difference equation based dynamic model has been established.
It is concluded that tools for the efficient development and testing of physically based dynamic models are of central importance for a model-based concurrent approach to the design of integrated engineering systems.
Tools for the efficient development and testing of physically based dynamic models are of central importance for this and include forward and inverse simulation, parameter sensitivity analysis, system identification, parameter estimation, optimisation and partial system testing through hardware-in-the-loop simulation.
A general protocol is presented for developing a detailed network-based dynamic model for S. oneidensis based on the Lumped Hybrid Cybernetic Model (L-HCM) framework.
We present a new, catchment-scale, process-based dynamic model for simulating mercury (Hg) in soils and surface waters.
Conversely, a physics-based dynamic model for the fine system is derived from a non-parametric system identification in order to match the experimental data well.
An artificial neural network (ANN -based dynANN -based for an experimental variable speedynamict expansion (DX) air conditioning (A/C) systemodel been developed, linking the indoor air temperature and humidity controlled by the DX A/C system with the variations oforompressor and supply fan spexperimental
In this study, considering the effects of entrainment and excess pore water pressure, a physics-based dynamic model for landslide is proposed in order to further study the dynamic mechanism of the landslide.
In this paper, a physics-based dynamic model based on Savage–Hutter theory for landslide has been presented.
Differential algebraic equation based dynamic sub-models are developed for key components, including compressors, internal heat exchangers, a storage chamber and turbines.
