Parametric shape optimization for combined additive–subtractive manufacturing

The final publication is available at Springer via http://dx.doi.org/10.1007/s11837-019-03886-xIn industrial practice, additive manufacturing (AM) processes are often followed by post-processing operations such as heat treatment, subtractive machining, milling, etc., to achieve the desired surface quality and dimensional accuracy. Hence, a given part must be 3D-printed with extra material to enable this finishing phase. This combined additive/subtractive technique can be optimized to reduce manufacturing costs by saving printing time and reducing material and energy usage. In this work, a numerical methodology based on parametric shape optimization is proposed for optimizing the thickness of the extra material, allowing for minimal machining operations while ensuring the finishing requirements. Moreover, the proposed approach is complemented by a novel algorithm for generating inner structures to reduce the part distortion and its weight. The computational effort induced by classical constrained optimization methods is alleviated by replacing both the objective and constraint functions by their sparse grid surrogates. Numerical results showcase the effectiveness of the proposed approach.Peer ReviewedPostprint (published version

Finite Element Modeling Driven by Health Care and Aerospace Applications

This thesis concerns the development, analysis, and computer implementation of mesh generation algorithms encountered in finite element modeling in health care and aerospace. The finite element method can reduce a continuous system to a discrete idealization that can be solved in the same manner as a discrete system, provided the continuum is discretized into a finite number of simple geometric shapes (e.g., triangles in two dimensions or tetrahedrons in three dimensions). In health care, namely anatomic modeling, a discretization of the biological object is essential to compute tissue deformation for physics-based simulations. This thesis proposes an efficient procedure to convert 3-dimensional imaging data into adaptive lattice-based discretizations of well-shaped tetrahedra or mixed elements (i.e., tetrahedra, pentahedra and hexahedra). This method operates directly on segmented images, thus skipping a surface reconstruction that is required by traditional Computer-Aided Design (CAD)-based meshing techniques and is convoluted, especially in complex anatomic geometries. Our approach utilizes proper mesh gradation and tissue-specific multi-resolution, without sacrificing the fidelity and while maintaining a smooth surface to reflect a certain degree of visual reality. Image-to-mesh conversion can facilitate accurate computational modeling for biomechanical registration of Magnetic Resonance Imaging (MRI) in image-guided neurosurgery. Neuronavigation with deformable registration of preoperative MRI to intraoperative MRI allows the surgeon to view the location of surgical tools relative to the preoperative anatomical (MRI) or functional data (DT-MRI, fMRI), thereby avoiding damage to eloquent areas during tumor resection. This thesis presents a deformable registration framework that utilizes multi-tissue mesh adaptation to map preoperative MRI to intraoperative MRI of patients who have undergone a brain tumor resection. Our enhancements with mesh adaptation improve the accuracy of the registration by more than 5 times compared to rigid and traditional physics-based non-rigid registration, and by more than 4 times compared to publicly available B-Spline interpolation methods. The adaptive framework is parallelized for shared memory multiprocessor architectures. Performance analysis shows that this method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting. The last part of this thesis focuses on finite element modeling of CAD data. This is an integral part of the design and optimization of components and assemblies in industry. We propose a new parallel mesh generator for efficient tetrahedralization of piecewise linear complex domains in aerospace. CAD-based meshing algorithms typically improve the shape of the elements in a post-processing step due to high complexity and cost of the operations involved. On the contrary, our method optimizes the shape of the elements throughout the generation process to obtain a maximum quality and utilizes high performance computing to reduce the overheads and improve end-user productivity. The proposed mesh generation technique is a combination of Advancing Front type point placement, direct point insertion, and parallel multi-threaded connectivity optimization schemes. The mesh optimization is based on a speculative (optimistic) approach that has been proven to perform well on hardware-shared memory. The experimental evaluation indicates that the high quality and performance attributes of this method see substantial improvement over existing state-of-the-art unstructured grid technology currently incorporated in several commercial systems. The proposed mesh generator will be part of an Extreme-Scale Anisotropic Mesh Generation Environment to meet industries expectations and NASA\u27s CFD visio

Optimization-based Methods in High-Order Mesh Generation and Untangling

High-order numerical methods for solving PDEs have the potential to deliver higher solution accuracy at a lower cost than their low-order counterparts. To fully leverage these high-order computational methods, they must be paired with a discretization of the domain that accurately captures key geometric features. In the presence of curved boundaries, this requires a high-order curvilinear mesh. Consequently, there is a lot of interest in high-order mesh generation methods. The majority of such methods warp a high-order straight-sided mesh through the following three-step process. First, they add additional nodes to a low-order mesh to create a high-order straight-sided mesh. Second, they move the newly added boundary nodes onto the curved domain (i.e., apply a boundary deformation). Finally, they compute the new locations of the interior nodes based on the boundary deformation. We have developed a mesh warping framework based on optimal weighted combinations of nodal positions. Within our framework, we develop methods for optimal affine and convex combinations of nodal positions, respectively. We demonstrate the effectiveness of the methods within our framework on a variety of high-order mesh generation examples in two and three dimensions. As with many other methods in this area, the methods within our framework do not guarantee the generation of a valid mesh. To address this issue, we have also developed two high-order mesh untangling methods. These optimization-based untangling methods formulate unconstrained optimization problems for which the objective functions are based on the unsigned and signed angles of the curvilinear elements. We demonstrate the results of our untangling methods on a variety of two-dimensional triangular meshes

Proceedings, MSVSCC 2014

Proceedings of the 8th Annual Modeling, Simulation & Visualization Student Capstone Conference held on April 17, 2014 at VMASC in Suffolk, Virginia

Deep Model for Improved Operator Function State Assessment

A deep learning framework is presented for engagement assessment using EEG signals. Deep learning is a recently developed machine learning technique and has been applied to many applications. In this paper, we proposed a deep learning strategy for operator function state (OFS) assessment. Fifteen pilots participated in a flight simulation from Seattle to Chicago. During the four-hour simulation, EEG signals were recorded for each pilot. We labeled 20- minute data as engaged and disengaged to fine-tune the deep network and utilized the remaining vast amount of unlabeled data to initialize the network. The trained deep network was then used to assess if a pilot was engaged during the four-hour simulation

A Unified Framework for Parallel Anisotropic Mesh Adaptation

Finite-element methods are a critical component of the design and analysis procedures of many (bio-)engineering applications. Mesh adaptation is one of the most crucial components since it discretizes the physics of the application at a relatively low cost to the solver. Highly scalable parallel mesh adaptation methods for High-Performance Computing (HPC) are essential to meet the ever-growing demand for higher fidelity simulations. Moreover, the continuous growth of the complexity of the HPC systems requires a systematic approach to exploit their full potential. Anisotropic mesh adaptation captures features of the solution at multiple scales while, minimizing the required number of elements. However, it also introduces new challenges on top of mesh generation. Also, the increased complexity of the targeted cases requires departing from traditional surface-constrained approaches to utilizing CAD (Computer-Aided Design) kernels. Alongside the functionality requirements, is the need of taking advantage of the ubiquitous multi-core machines. More importantly, the parallel implementation needs to handle the ever-increasing complexity of the mesh adaptation code. In this work, we develop a parallel mesh adaptation method that utilizes a metric-based approach for generating anisotropic meshes. Moreover, we enhance our method by interfacing with a CAD kernel, thus enabling its use on complex geometries. We evaluate our method both with fixed-resolution benchmarks and within a simulation pipeline, where the resolution of the discretization increases incrementally. With the Telescopic Approach for scalable mesh generation as a guide, we propose a parallel method at the node (multi-core) for mesh adaptation that is expected to scale up efficiently to the upcoming exascale machines. To facilitate an effective implementation, we introduce an abstract layer between the application and the runtime system that enables the use of task-based parallelism for concurrent mesh operations. Our evaluation indicates results comparable to state-of-the-art methods for fixed-resolution meshes both in terms of performance and quality. The integration with an adaptive pipeline offers promising results for the capability of the proposed method to function as part of an adaptive simulation. Moreover, our abstract tasking layer allows the separation of different aspects of the implementation without any impact on the functionality of the method

Logical partitioning of parallel system simulations

Simulation has been a fundamental tool to prototype, hypothesize, and evaluate new ideas to continue improving system performance. However, increasing levels of processor parallelism and heterogeneity have introduced additional constraints when evaluating new designs. The work embodied in this dissertation explores how to leverage novel ideas in simulator partitioning to improve simulator speed and flexibility for simulating these new types of systems. The contribution of this work includes the introduction of optimistic partitioned simulation to improve parallelization, and the introduction of warped partitioned simulation for improved flexibility. These ideas are refined and demonstrated through the use of prototypes to demonstrate their benefits compared to state-of-the-art approaches. By leveraging partitioning in a structured manner, it is possible to design simulators that better address the open challenges of parallel and heterogeneous systems design.Electrical and Computer Engineerin

Cluster Analysis of Time Series Data with Application to Hydrological Events and Serious Illness Conversations

Cluster analysis explores the underlying structure of data and organizes it into groups (i.e., clusters) such that observations within the same group are more similar than those in different groups. Quantifying the ``similarity\u27\u27 between observations, choosing the optimal number of clusters, and interpreting the results all require careful consideration of the research question at hand, the model parameters, the amount of data and their attributes. In this dissertation, the first manuscript explores the impact of design choices and the variability in clustering performance on different datasets. This is demonstrated through a benchmark study consisting of 128 datasets from the University of California, Riverside time series classification archive. Next, a multivariate event time series clustering approach is applied to hydrological storm events in watershed science. Specifically, river discharge and suspended sediment data from six watersheds in the Vermont are clustered, and yield four types of hydrological water quality events to help inform conservation and management efforts. In a second application, a novel and computationally efficient clustering algorithm called SOMTimeS (Self-organizing Map for Time Series) is designed for large time series analysis using dynamic time warping (DTW). The algorithm scales linearly with increasing data, making SOMTimeS, to the best of our knowledge, the fastest DTW-based clustering algorithm to date. For proof of concept, it is applied to conversational features from a Palliative Care Communication Research Initiative study with the goal of understanding and motivating high quality communication in serious illness health care settings