Abstract
Power plant modeling plays a key role in many purposes, like process design assessment, the assessment, and prediction of plant performance, operating procedure evaluation, control system design, and system prognosis and diagnosis. The present chapter introduces the discipline of 0D/1D modeling applied to thermal hydraulics and their main applications to real-life systems: how 0D/1D modeling relates to the 3D physical equations, what are the fundamental assumptions underlying 0D/1D physical models and the main limitations of the numerical solvers commonly used for such models, what is the rationale for a 0D/1D component models library and what kinds of real-life systems can be modeled and simulated for different purposes (plant sizing, control, operation and maintenance, prognosis, diagnosis and monitoring). Also, in this chapter, many questions are answered: what is a system, what is a model and modeling, what is simulation and why is modeling important?
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- 1.
The word causality in causality analysis should not be confounded with the word causality in physical causality which means that causes always precede their effects. However, there is a relationship between the two notions. The objective of causality analysis is to assign each unknown variable to a unique equation that computes this variable and vice versa. State derivatives are assigned in the most obvious way to equations such as (1.7a). Such assignments are conformant with physical causality as state derivatives (predictors) are thus computed from the state past values. However, algebraic variables are assigned to equations such as (1.7b) whose physical causalities are lost as algebraic equations are obtained by neglecting the dynamics of the system that force the physical causalities. The result of the analysis may, thus, not reflect the physical causality of the real system for the algebraic variables. This is why algebraic variables should not be used in a model when causalities are important, such as the feedback loop of a control system.
References
Bouskela D (2016) Multi-mode physical modelling of a drum boiler, complex adaptive systems. Proc Comput Sci 95:516–523
Cloutier R, Baldwin C, Bone MA (2015) Systems engineering simplified. CRC Press, Taylor & Francis
Elmqvist H, Mattsson SE, Otter M (2014) Modelica extensions for multi-mode DAE systems. In: Proceedings of the 10th international Modelica conference
EPRI (2016) Electric Power System Flexibility, challenges and opportunity. Available from https://www.epri.com/#/pages/product/3002007374/?lang=en
MODRIO project (2012–1016), ITEA 2 11004 MODRIO. Available from https://github.com/modelica/modrio and https://www.modelica.org/external-projects/modrio
Swinbank R, Shutyaev V, Lahoz WA (2003) Data assimilation for the earth system. In: Series IV: earth end environmental sciences, vol 26. Kluwer
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El Hefni, B., Bouskela, D. (2019). Introduction to Modeling and Simulation. In: Modeling and Simulation of Thermal Power Plants with ThermoSysPro . Springer, Cham. https://doi.org/10.1007/978-3-030-05105-1_1
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DOI: https://doi.org/10.1007/978-3-030-05105-1_1
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