Search

The combinatorial nature of powertrain system design problems challenges system engineers as powertrain components can be interconnected, sized, and controlled in numerous ways. Hence, finding promising candidates considering, e.g., fuel economy, drivability, and complexity requires a systematic approach. A dedicated and novel framework for computational design synthesis is, therefore, presented in this paper. The underlying aim of this framework is to enable full-automated powertrain system optimization over multiple system layers, including topology selection, component sizing, and optimal control, and follows the principles of platform-based design. Based on a library of components, system topologies are automatically generated by solving a constraint satisfaction problem. Meanwhile, constraints originating from customers’ requirements, application-specific design rules, and physical laws are respected. Furthermore, the topology generation can be applied to hierarchical decomposed systems on multiple system levels in order to reach the full potential of system design. Using a relevant automotive use case, it is demonstrated that possible topologies for powertrains equipped with a complex continuously variable transmission are automatically synthesized. Next, simulation-based evaluation (automated physical modeling and filtering) of these candidates by this framework results in a set of feasible topologies, satisfying the required functionality and physical constructibility. Along with this topology evaluation phase, a control-actuation scheme and an initial set of feasible component parameters are obtained that are required for full-automated multilayer optimization, which is seen as the next novel future step. The presented framework leads to novel and innovative powertrain and transmission designs with a total amount of 635 feasible transmission topologies based on maximum 13 components, whereas the overall time required to generate and evaluate all initial possible candidates (2.5 $\cdot 10^{27}$) is relatively short, i.e., 5.5 and 20 h, respectively. [ABSTRACT FROM AUTHOR]

Summary: Computational simulation is becoming increasingly important in numerous research fields. Depending on the modeled system, several methods such as differential equations or Monte‐Carlo simulations may be used to represent the system behavior. The amount of computation and memory needed to run a simulation depends on its size and precision, and large simulations usually lead to long runs, thus requiring to adapt the model to a parallel system. Complex systems are often simulated using multi‐agent systems (MASs). While linear system based models benefit from a large set of tools to take advantage of parallel resources, multi‐agent systems suffer from a lack of platforms that ease the use of such resources. In this paper, we propose the use of Nested Graphs for a new modeling approach that allows the design of large, complex, and multi‐scale multi‐agent models, which can efficiently be distributed on parallel resources. Nested Graphs are formally defined and are illustrated on the well‐known predator‐prey model. We also introduce PDMAS (parallel and distributed multi‐agent system): a platform that implements the Nested Graph modeling approach to ease the distribution of multi‐agent models on High Performance Computing clusters. Performance results are presented to validate the efficiency of the resulting models. [ABSTRACT FROM AUTHOR]

In this paper we focus on connected signed graphs of fixed number of vertices, positive edges and negative edges that maximize the largest eigenvalue (also called the index) of their adjacency matrix. In the first step we determine these signed graphs in the set of signed generalized theta graphs. Concerning the general case, we use the eigenvector techniques for getting some structural properties of resulting signed graphs. In particular, we prove that positive edges induce nested split subgraphs, while negative edges induce double nested signed subgraphs.We observe that our concept can be applied when considering balancedness of signed graphs (the property that is extensively studied in both mathematical and non-mathematical context). [ABSTRACT FROM AUTHOR]

Shapely nested graph transformation is the computational model for DiaPlan, a language for programming with graphs representing diagrams that is currently being developed. The model supports nested structuring of graphs, structural graph types (shapes), and rules with graph variables. We extend this model by two concepts that are essential for programming: abstraction allows compound transformations to be named and parameterized; control allows the order of rule application to be specified. These concepts refine the computational model with respect to structuring and efficiency needs of a programming language while preserving its rule-based and graph-ical nature. [ABSTRACT FROM AUTHOR]