Development of a CAD Model Simplification Framework for Finite Element Analysis

Analyzing complex 3D models using finite element analysis software requires suppressing features/parts that are not likely to influence the analysis results, but may significantly improve the computational performance both in terms of mesh size and mesh quality. The suppression step often depends on the context and application. Currently, most analysts perform this step manually. This step can take a long time to perform on a complex model and can be tedious in nature. The goal of this thesis was to generate a simplification framework for both part and assembly CAD models for finite element analysis model preparation. At the part level, a rule-based approach for suppressing holes, rounds, and chamfers is presented. Then a tool for suppressing multiple specified part models at once is described at the assembly level. Upon discussion of the frameworks, the tools are demonstrated on several different models to show the complete approach and the computational performances. The work presented in this thesis is expected to significantly reduce the manual time consuming activities within the model simplification stage. This is accomplished through multiple feature/part suppression compared to the industry standard of suppressing one feature/part at a time. A simplified model speeds up the overall analysis, reducing the meshing time and calculation of the analysis values, while maintaining and on occasion improving the quality of the analysis

PHYSICS-AWARE MODEL SIMPLIFICATION FOR INTERACTIVE VIRTUAL ENVIRONMENTS

Rigid body simulation is an integral part of Virtual Environments (VE) for autonomous planning, training, and design tasks. The underlying physics-based simulation of VE must be accurate and computationally fast enough for the intended application, which unfortunately are conflicting requirements. Two ways to perform fast and high fidelity physics-based simulation are: (1) model simplification, and (2) parallel computation. Model simplification can be used to allow simulation at an interactive rate while introducing an acceptable level of error. Currently, manual model simplification is the most common way of performing simulation speedup but it is time consuming. Hence, in order to reduce the development time of VEs, automated model simplification is needed. The dissertation presents an automated model simplification approach based on geometric reasoning, spatial decomposition, and temporal coherence. Geometric reasoning is used to develop an accessibility based algorithm for removing portions of geometric models that do not play any role in rigid body to rigid body interaction simulation. Removing such inaccessible portions of the interacting rigid body models has no influence on the simulation accuracy but reduces computation time significantly. Spatial decomposition is used to develop a clustering algorithm that reduces the number of fluid pressure computations resulting in significant speedup of rigid body and fluid interaction simulation. Temporal coherence algorithm reuses the computed force values from rigid body to fluid interaction based on the coherence of fluid surrounding the rigid body. The simulations are further sped up by performing computing on graphics processing unit (GPU). The dissertation also presents the issues pertaining to the development of parallel algorithms for rigid body simulations both on multi-core processors and GPU. The developed algorithms have enabled real-time, high fidelity, six degrees of freedom, and time domain simulation of unmanned sea surface vehicles (USSV) and can be used for autonomous motion planning, tele-operation, and learning from demonstration applications

Short time-series microarray analysis: Methods and challenges

The detection and analysis of steady-state gene expression has become routine. Time-series microarrays are of growing interest to systems biologists for deciphering the dynamic nature and complex regulation of biosystems. Most temporal microarray data only contain a limited number of time points, giving rise to short-time-series data, which imposes challenges for traditional methods of extracting meaningful information. To obtain useful information from the wealth of short-time series data requires addressing the problems that arise due to limited sampling. Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain. This commentary addresses recent advances in methods for short-time series analysis including simplification-based approaches and the integration of multi-source information. Nevertheless, further studies and development of computational methods are needed to provide practical solutions to fully exploit the potential of this data

Supporting Story Synthesis: Bridging the Gap between Visual Analytics and Storytelling

Visual analytics usually deals with complex data and uses sophisticated algorithmic, visual, and interactive techniques. Findings of the analysis often need to be communicated to an audience that lacks visual analytics expertise. This requires analysis outcomes to be presented in simpler ways than that are typically used in visual analytics systems. However, not only analytical visualizations may be too complex for target audience but also the information that needs to be presented. Hence, there exists a gap on the path from obtaining analysis findings to communicating them, which involves two aspects: information and display complexity. We propose a general framework where data analysis and result presentation are linked by story synthesis, in which the analyst creates and organizes story contents. Differently, from the previous research, where analytic findings are represented by stored display states, we treat findings as data constructs. In story synthesis, findings are selected, assembled, and arranged in views using meaningful layouts that take into account the structure of information and inherent properties of its components. We propose a workflow for applying the proposed framework in designing visual analytics systems and demonstrate the generality of the approach by applying it to two domains, social media, and movement analysis

Measuring and improving the readability of network visualizations

Network data structures have been used extensively for modeling entities and their ties across such diverse disciplines as Computer Science, Sociology, Bioinformatics, Urban Planning, and Archeology. Analyzing networks involves understanding the complex relationships between entities as well as any attributes, statistics, or groupings associated with them. The widely used node-link visualization excels at showing the topology, attributes, and groupings simultaneously. However, many existing node-link visualizations are difficult to extract meaning from because of (1) the inherent complexity of the relationships, (2) the number of items designers try to render in limited screen space, and (3) for every network there are many potential unintelligible or even misleading visualizations. Automated layout algorithms have helped, but frequently generate ineffective visualizations even when used by expert analysts. Past work, including my own described herein, have shown there can be vast improvements in network visualizations, but no one can yet produce readable and meaningful visualizations for all networks. Since there is no single way to visualize all networks effectively, in this dissertation I investigate three complimentary strategies. First, I introduce a technique called motif simplification that leverages the repeating patterns or motifs in a network to reduce visual complexity. I replace common, high-payoff motifs with easily understandable glyphs that require less screen space, can reveal otherwise hidden relationships, and improve user performance on many network analysis tasks. Next, I present new Group-in-a-Box layouts that subdivide large, dense networks using attribute- or topology-based groupings. These layouts take group membership into account to more clearly show the ties within groups as well as the aggregate relationships between groups. Finally, I develop a set of readability metrics to measure visualization effectiveness and localize areas needing improvement. I detail optimization recommendations for specific user tasks, in addition to leveraging the readability metrics in a user-assisted layout optimization technique. This dissertation contributes an understanding of why some node-link visualizations are difficult to read, what measures of readability could help guide designers and users, and several promising strategies for improving readability which demonstrate that progress is possible. This work also opens several avenues of research, both technical and in user education

Persistent Homology Guided Force-Directed Graph Layouts

Graphs are commonly used to encode relationships among entities, yet their abstractness makes them difficult to analyze. Node-link diagrams are popular for drawing graphs, and force-directed layouts provide a flexible method for node arrangements that use local relationships in an attempt to reveal the global shape of the graph. However, clutter and overlap of unrelated structures can lead to confusing graph visualizations. This paper leverages the persistent homology features of an undirected graph as derived information for interactive manipulation of force-directed layouts. We first discuss how to efficiently extract 0-dimensional persistent homology features from both weighted and unweighted undirected graphs. We then introduce the interactive persistence barcode used to manipulate the force-directed graph layout. In particular, the user adds and removes contracting and repulsing forces generated by the persistent homology features, eventually selecting the set of persistent homology features that most improve the layout. Finally, we demonstrate the utility of our approach across a variety of synthetic and real datasets

Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador

[EN] Several efforts have been devoted to developing sustainable cities to address global environmental challenges and the growth of urban areas. In particular, transportation has various issues such as air pollution, noise, and traffic, which have to be addressed by collecting significant information of the traffic and road conditions of the cities. Automating the data extraction process and street network construction will allow building more useful models to study traffic behavior. This work presents a network modeling approach to identify interest points (extreme and internal) of the city, through a winner-takes-all edge trimming, and mapping the traffic flow between them. Such points can be considered as entries of an Origin-Destination matrix, where such information can be used to model traffic behavior between interest points. The case study of Quito, Ecuador is considered. Besides, to address environmental issues, this paper encourages the replacement of internal combustion taxis with electric vehicles. From the understanding of the vehicle traffic behavior, a pre-feasibility siting of electric taxi (ET) charging stations was carried out. The results will allow performing the sizing of each charging station considering electric power network constraints. This work aims to ensure a sustainable transportation system based on this crucial information.This work was supported in part by the Universidad de Las Americas, Quito, Ecuador, under Project SIS.JCG.19.03 and Project SIS.MGR.20.01, and in part by the CYTED Network Ibero-American Thematic Network on ICT Applications for Smart Cities under Grant 518RT0559.González-Rodríguez, MS.; Clairand, J.; Soto-Espinosa, K.; Jaramillo-Fuelantala, J.; Escrivá-Escrivá, G. (2020). Urban Traffic Flow Mapping of an Andean Capital: Quito, Ecuador. IEEE Access. 8:195459-195471. https://doi.org/10.1109/ACCESS.2020.3033518S195459195471