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In the present paper we address the problem of optimal wall-shape design of a single phase laminar thermosyphon loop. The model takes the buoyancy forces into account via the Boussinesq approximation. We focus our study on showing the effects of wall shape on the flow and on the temperature inside the thermosyphon. To this extend we determine the dependency of the flow rate and the increase in temperature, on the geometrical characteristics of the loop. The geometry considered is a set of axially symmetric corrugated pipes described by a set of parameters; namely the pipe inner radius, the period of the corrugation, the amplitude of the corrugation, and the ratio of expansion and contraction regions of a period of the pipe. The governing equations are solved using the Finite Element Method, in combination with an adaptive mesh refinement technique in order to capture the effects of wall shape. We characterize the effects of the amplitude and of the ratio of expansion and contraction. In particular we show that for a given fixed amplitude it is possible to find an optimal ratio of expansion and contraction that minimizes the temperature inside the thermosyphon. The results show that by adequately choosing the design parameters, the performance of the thermosyphon loop can be improved.

This paper presents the recent results of the integration of computational and conceptual solutions for the complex case of environmental information management. The solution for the major goal of creating and developing long-term multi-disciplinary knowledge resources and conceptual and computational support was achieved by implementing and integrating key components. The key components are long-term knowledge resources providing required structures for universal knowledge creation, documentation, and preservation, universal multi-disciplinary and multi-lingual conceptual knowledge and classification, especially, references to Universal Decimal Classification (UDC), sustainable workflows for environmental information management, and computational support for dynamical use, processing, and advanced scientific computing with Integrated Information and Computing System (IICS) components and High End Computing (HEC) resources.

This paper presents the up-to-date results concerning the integration of scientific classification and computation deploying long-term knowledge resources. The knowledge resources are the central component in universal multi-disciplinary and multi-lingual knowledge management. While the research focus is on universal knowledge creation, documentation, and preservation, the architecture allows efficient dynamical use and processing as well as computational utilisation, e.g., in advanced scientific computing with Integrated Information and Computing System (IICS) components and High End Computing (HEC) resources. The knowledge resources’ goal is to address a major gap for long-term knowledge and to provide structure, references, and classification for any type of data, knowledge, and context. Based on the implementation, the case study depicts concepts of references to Universal Decimal Classification (UDC) views, e.g., for workflows. The key results are benefits for knowledge management and documentation, the sustainable integration of long-term documentation, data, and workflow vitality for improved knowledge gathering, computational purposes, and evaluation based on big data.

The arrival of hardware accelerators has created a new gold rush to be the first to deliver their promise of high performance for numerical applications. Despite the recent advances in programmability, it is still hard to develop tuned programs that extract all the potential performance promised by the manufacturers. In this paper we remind the community that while this development effort is a noble endeavor, there is a lot of low hanging fruit that can be harvested easily. Picking this low hanging fruit benefits the scientific computing community immediately and prototypes the approach that further optimizations may follow. We demonstrate this by focusing on a widely used set of operations, the level-3 BLAS, targeting the NVIDIA GPUs.

Software development in scientific computing typically deals with a large number of algorithms. An algorithm can have different implementations due to specializations for certain cases. These specializations may result in different return types. If the return types are not known a‐priori, meaning, the types cannot be derived during compile‐time, run‐time techniques are required. This topic is generally referred to as covariant return types and is typically related to the object‐oriented programming domain. Common approaches are based on dynamic polymorphism techniques which result in run‐time overhead. We present an approach to handle non‐a‐priori known return types without the need of dynamic polymorphism techniques, hence, increasing run‐time efficiency. The approach is discussed in detail based on an example in the field of computational geometry.

The open‐source software R has become one of the most widely used systems for statistical data analysis and for making graphs, but it is also well suited for other disciplines in scientific computing. One of the fields where considerable progress has been made is the solution of differential equations. Here we first give an overview of the types of differential equations that R can solve, and then demonstrate how to use R for solving a 2‐Dimensional partial differential equation.