LAUSR

Abstract Generally, the 2nd Law is stated in negative, passive terms, in a way that says what cannot happen, or what the best possible outcome could be, or what is an inevitable (e.g., inopportune) consequence. Granted, there has been productive usage of the exergy concept, to do ‘2nd Law Analysis’. Granted, there are statements saying that a consequence of every process is the generation of entropy. And there have been many efforts aiming to use an entropy statement to do ‘2nd Law Modeling’ – to predict the path of processes. While there have been specific successes, there are issues. For example, in some instances a success depends upon an extremization that maximizes the entropy generation (or the rate thereof), while in other instances, minimization. Though there may be criteria for determining whether maximization or minimization is appropriate in a particular application, the need for such a criterion points out a weakness. Proposed here is this: Every process is a consequence of a cause, and to assertively predict the path of a process is to determine consequences of a cause. Entropy production is not a cause but only a consequence. The 2nd Law statement to be proposed herein employs a common measure of cause, namely Gibbs' available energy, and how the expenditure of that cause ensues in a process. Therein lies an underlying principle for modeling the process path (whether macro, nano, bio or micro). Three rudimentary examples of the application of this ‘2nd Law principle’ for modeling processes are presented in this paper; one for a transient ‘heat transfer’ process, one for a ‘mechanical’ process, and a third for a combined thermal and pneumatic process. It is shown how available energy provides a key to modeling processes of different ‘types’ – heat transfer, mechanics, pneumatics. Heretofore each type required distinct ‘principles’ (and courses of study). To show the authenticity of the results obtained with this ‘2nd Law modeling’, they are compared with results obtained with the ‘traditional’ models.