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The study of vortex breakdown gives rise to an interest in waves in swirling flow. Our interest is centered on the existence of both solitary and periodic internal waves. In this report a model physical probelm is studied in a mathematically exact formulation. We restrict our attention to an incompressible, inviscid fluid swirling through a right cylinder of infinite length and finite radius. Our theory, which is not restricted to small amplitudes, predicts both waves of elevation and depression, depending on the angular velocity (swirl) distribution and the velocity distribution at infinity. Just as for the classical surface solitary waves, these internal solitary waves are single crested, symmetric, and decay exponentially away from the crest. Hence they represent disturbances of essentially finite extent. Variational techniques and the theory of rearrangements are used to demonstrate these qualitative features. Moreover, we show that the solitary internal wave arises as a limit of periodic internal waves of increasing wave lengths. Variational techniques are used to demonstrate that the Euler equations possess solutions that represent progressing waves of permanent form. Moreover, internal solitary wave solutions are shown to arise as the limiting forms of internal periodic waves as the period length becomes unbounded.

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container’s image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.

Computational science has been greatly improved by the use of containers for packaging software and data dependencies. In a scholarly context, the main drivers for using these containers are transparency and support of reproducibility; in turn, a workflow’s reproducibility can be greatly affected by the choices that are made with respect to building containers. In many cases, the build process for the container’s image is created from instructions provided in a Dockerfile format. In support of this approach, we present a set of rules to help researchers write understandable Dockerfiles for typical data science workflows. By following the rules in this article, researchers can create containers suitable for sharing with fellow scientists, for including in scholarly communication such as education or scientific papers, and for effective and sustainable personal workflows.