Artificial Intelligence - Special volume on qualitative reasoning about physical systems
Artificial Intelligence
Compositional modeling: finding the right model for the job
Artificial Intelligence - Special issue: Qualitative reasoning about physical systems II
Finding stable causal interpretations of equations
Recent advances in qualitative physics
Qualitative reasoning with microscopic theories
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 1
Qualitative order of magnitude energy-flow-based failure modes and effects analysis
Journal of Artificial Intelligence Research
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Choosing between multiple ontological perspectives is crucial for reasoning about the physical world. Choosing the wrong perspective can make a reasoning task impossible. This paper introduces a Lagrangian plug flow ontology (PF) for reasoning about thermodynamic fluid flow. We show that this ontology captures continuously changing behaviors of flowing fluids not represented in currently implemented ontologies. These behaviors are essential for understanding thermodynamic applications such as power cycles, refrigeration, liquefaction, throttling and flow through nozzles. We express the ontology within the framework of Qualitative Process (QP) theory. To derive our QP theory for plug flow, we use the method of causal clustering to find causal interpretations of thermodynamic equations. We also incorporate qualitative versions of standard thermodynamic relations, including the second law of thermodynamics and Clapeyron's equation.