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

Adorym: A multi-platform generic x-ray image reconstruction framework based on automatic differentiation

We describe and demonstrate an optimization-based x-ray image reconstruction framework called Adorym. Our framework provides a generic forward model, allowing one code framework to be used for a wide range of imaging methods ranging from near-field holography to and fly-scan ptychographic tomography. By using automatic differentiation for optimization, Adorym has the flexibility to refine experimental parameters including probe positions, multiple hologram alignment, and object tilts. It is written with strong support for parallel processing, allowing large datasets to be processed on high-performance computing systems. We demonstrate its use on several experimental datasets to show improved image quality through parameter refinement

FAST PHYSICS-BASED SIMULATION OF VASCULAR SURGERY

Ph.DDOCTOR OF PHILOSOPH

REAL-TIME DATA MINING FOR PROCESS OPERATIONS USING GRAPHICS PROCESSING UNIT (GPU)-BASED HIGH PERFORMANCE COMPUTING

Ph.DDOCTOR OF PHILOSOPH

MASSIVELY PARALLEL ALGORITHMS FOR POINT CLOUD BASED OBJECT RECOGNITION ON HETEROGENEOUS ARCHITECTURE

With the advent of new commodity depth sensors, point cloud data processing plays an increasingly important role in object recognition and perception. However, the computational cost of point cloud data processing is extremely high due to the large data size, high dimensionality, and algorithmic complexity. To address the computational challenges of real-time processing, this work investigates the possibilities of using modern heterogeneous computing platforms and its supporting ecosystem such as massively parallel architecture (MPA), computing cluster, compute unified device architecture (CUDA), and multithreaded programming to accelerate the point cloud based object recognition. The aforementioned computing platforms would not yield high performance unless the specific features are properly utilized. Failing that the result actually produces an inferior performance. To achieve the high-speed performance in image descriptor computing, indexing, and matching in point cloud based object recognition, this work explores both coarse and fine grain level parallelism, identifies the acceptable levels of algorithmic approximation, and analyzes various performance impactors. A set of heterogeneous parallel algorithms are designed and implemented in this work. These algorithms include exact and approximate scalable massively parallel image descriptors for descriptor computing, parallel construction of k-dimensional tree (KD-tree) and the forest of KD-trees for descriptor indexing, parallel approximate nearest neighbor search (ANNS) and buffered ANNS (BANNS) on the KD-tree and the forest of KD-trees for descriptor matching. The results show that the proposed massively parallel algorithms on heterogeneous computing platforms can significantly improve the execution time performance of feature computing, indexing, and matching. Meanwhile, this work demonstrates that the heterogeneous computing architectures, with appropriate architecture specific algorithms design and optimization, have the distinct advantages of improving the performance of multimedia applications

Survey of scientific programming techniques for the management of data-intensive engineering environments

The present paper introduces and reviews existing technology and research works in the field of scientific programming methods and techniques in data-intensive engineering environments. More specifically, this survey aims to collect those relevant approaches that have faced the challenge of delivering more advanced and intelligent methods taking advantage of the existing large datasets. Although existing tools and techniques have demonstrated their ability to manage complex engineering processes for the development and operation of safety-critical systems, there is an emerging need to know how existing computational science methods will behave to manage large amounts of data. That is why, authors review both existing open issues in the context of engineering with special focus on scientific programming techniques and hybrid approaches. 1193 journal papers have been found as the representative in these areas screening 935 to finally make a full review of 122. Afterwards, a comprehensive mapping between techniques and engineering and nonengineering domains has been conducted to classify and perform a meta-analysis of the current state of the art. As the main result of this work, a set of 10 challenges for future data-intensive engineering environments have been outlined.The current work has been partially supported by the Research Agreement between the RTVE (the Spanish Radio and Television Corporation) and the UC3M to boost research in the field of Big Data, Linked Data, Complex Network Analysis, and Natural Language. It has also received the support of the Tecnologico Nacional de Mexico (TECNM), National Council of Science and Technology (CONACYT), and the Public Education Secretary (SEP) through PRODEP

A Coupled Lattice Boltzmann-Extended Finite Element Model for Fluid-Structure Interaction Simulation with Crack Propagation

Fatigue cracking of structures in fluid-structure interaction (FSI) applications is a pervasive issue that impacts a broad spectrum of engineering activities, ranging from large-scale ocean engineering and aerospace structures to bio-medical prosthetics. Fatigue is a particular concern in the offshore drilling industry where the problem is exacerbated by environmental degradation, and where structural failure can have substantial financial and environmental ramifications. As a result, interest has grown for the development of structural health monitoring (SHM) schemes for FSI applications that promote early damage detection. FSI simulation provides a practical and efficient means for evaluating and training SHM approaches for FSI applications, and for improving fatigue life predictions through robust parametric studies that address uncertainties in both crack propagation and FSI response. To this end, this paper presents a numerical modeling approach for simulating FSI response with crack propagation. The modeling approach couples a massively parallel lattice Boltzmann fluid solver, executed on a graphics processing unit (GPU) device, with an extended finite element (XFE) solid solver. Two-way interaction is provided by an immersed boundary coupling scheme, in which a Lagrangian solid mesh moves on top of a fixed Eulerian fluid grid. The theoretical basis and numerical implementation of the modeling approach are presented, along with a simple demonstration problem involving subcritical crack growth in a flexible beam subject to vortex-induced vibration