Computer-Aided Tolerancing (CAT) software has become the standard for statistically analyzing the effects of geometrical part variations on product quality. Irrespective of CAT’s scope and technical depth, Finite Element Analysis (FEA) software, used to simulate the physical product behavior for ideal part geometry in the first place, is also often used for studies with geometrical shapes deviating from their nominal. However, this requires a manual translation of the tolerances specified in the design phase into geometrical variations represented by Finite Element (FE) meshes and their transfer to the FEA software. The method presented in this article exploits the potential of Model-Based Definition by establishing a link between Computer-Aided Design and FEA software to empower the latter for variation simulation based on semantic Geometric Dimensioning and Tolerancing (GD&T) information. To transfer this information exchanged via the Quality Information Framework (QIF) standard, a new mapping algorithm is presented that automatically decomposes FE meshes into geometrical face elements and creates a semantic link with the GD&T information carried in QIF. As a result, geometrical features are simultaneously described through meshes with nodes in the 3D Euclidean space and mathematical geometrical faces in the 2D parameter space. Exploiting this duality, mesh deviations are modeled indirectly by adjusting the mapped feature descriptions. An exemplary implementation in ANSYS® and its usage for non-intrusive structural simulations illustrates that sharing tolerancing information via QIF enables an automated, GD&T standards-compliant variation simulation within FEA software environments and is one step closer to a seamless digital thread for geometry assurance.

An essential premise for a reliable variation simulation is that information on the geometrical part variations and their accumulation and propagation within an assembly is available, accessible, interchangeable, and usable in all geometry-related downstream activities. For this reason, this article studies the potential of the QIF (Quality Information Framework) standard. It illustrates how it can be used in the sense of Model-Based Definition to close gaps in the digital geometry assurance process. Besides benefits in the automation of variation simulation, it demonstrates that the semantic, feature-based linkage between product specification and inspection information in QIF 3.0 facilitates the augmentation of variation simulation with more detailed feature information for pre-production applications and feeding the digital twin to assure and optimize product quality in the production phase. • Enhancing variation simulation with the Quality Information Framework (QIF) standard. • Mapping “as-design” and “as-inspected” QIF 3.0 information to surface meshes for variation simulation. • Mesh segmentation with Boundary Representation information from QIF 3.0. • Using QIF 3.0 for statistical predictions and digital twin applications in geometry assurance.

In this paper we present an approach for an information system which is capable of processing and analysing vast amounts of data. In addition to Big Data solutions we do not focus on ex post batch processing but on online stream processing. We use Apache Storm in combination with Complex Event Processing to provide a scalable and dynamic event-driven information system, providing logistics businesses with relevant information in real-time to increase their data and process transparency.

An analytical framework is presented to study the self-adaptive behavior of probabilistic routing protocols for computer networks. Such soft routing protocols have attracted attention for delivering packets more reliably, robustly, and efficiently than conventional deterministic approaches. Efficient global operating parameters can be estimated without resorting to expensive Monte-Carlo simulation of the whole system. Key model parameters are routing sensitivity and routing threshold/noise, which control the "randomness" of packet routes between source and destination, and a metric estimator. Global network characteristics are estimated, including steady state routing probabilities, average path length, and path robustness. The framework is based on a Markov chain analysis. Individual network nodes are represented as states. Standard techniques are used to find primary statistics of the steady state global routing pattern, given a set of link costs. The use of packets to collect information about, or "sample," the network for new path information is also reviewed. How the network sample rate influences performance is investigated.

Two link models are used to generate a social proximity network topology, used to support delay tolerant networking in human networks. It is shown how the link models produce graphs with fundamentally different properties, though based on the same underlying social mobility data. These properties influence how messages are routed, among other global attributes. Network design must consider link models as a fundamental aspect of the architecture.

A biologically inspired algorithm is presented to route messages in mobile wireless ad-hoc networks. The principles of swarm intelligence are used to define a probabilistic algorithm for which routing through paths of maximum throughput is an emergent property. This adaptive algorithm, dubbed termite, uses stigmergy to reduce the amount of control traffic needed to maintain a high data goodput. Stigmergy is a process by which information is indirectly communicated between individuals through their environment The termite environment is the contents of all routing tables. The movement of packets is influenced at each node, and communicating nodes observe this influence to update their own tables. Strong routing robustness is achieved through the use of multiple paths; each packet is routed randomly and independently.

We propose an elementary operation on a pair of vector fields as a building block for defining and computing global line-type features of vector or scalar fields. While usual feature definitions often are procedural and therefore implicit, our operator allows precise mathematical definitions. It can serve as a basis for comparing feature definitions and for reuse of algorithms and implementations. Applications focus on vortex core methods.

This paper presents a novel method to extract vortical structures from 3D CFD (computational fluid dynamics) vector fields automatically. It discusses the underlying theory and some aspects of the implementation. Making use of higher-order derivatives, the method is able to locate bent vortices. In order to structure the recognition procedure, we distinguish locating the core line from calculating attributes of strength and quality. Results are presented on several flow fields from the field of turbomachinery.

Visualization of CFD data for turbomachinery design poses some special requirements which are often not addressed by standard flow visualization systems. The authors discuss the issues involved with this particular application and its requirements with respect to flow visualization. Aiming at a feature-based visualization for this task, they examine various existing techniques to locate vortices. The specific flow conditions for turbomachines demonstrate limitations of current methods. Visualization of turbomachinery flow thus raises some challenges and research topics, particularly regarding feature extraction.