  We demonstrate that thermodynamic knowledge acquired by humans can be
transferred to computers so that the machine can use it to solve thermodynamic
problems and produce explainable solutions with a guarantee of correctness. The
actionable knowledge representation system that we have created for this
purpose is called KnowTD. It is based on an ontology of thermodynamics that
represents knowledge of thermodynamic theory, material properties, and
thermodynamic problems. The ontology is coupled with a reasoner that sets up
the problem to be solved based on user input, extracts the correct, pertinent
equations from the ontology, solves the resulting mathematical problem, and
returns the solution to the user, together with an explanation of how it was
obtained. KnowTD is presently limited to simple thermodynamic problems, similar
to those discussed in an introductory course in Engineering Thermodynamics.
This covers the basic theory and working principles of thermodynamics. KnowTD
is designed in a modular way and is easily extendable.
