Department of Mechanical Engineering

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https://hdl.handle.net/10919/23261
The Virginia Tech Mechanical Engineering Department serves its students, alumni, the Commonwealth of Virginia, and the nation through a variety of academic, research and service activities. Our missions are to: holistically educate our students for professional leadership as creative problem-solvers in a diverse society, conduct advanced research for societal advancement, train graduate students for scholarly inquiry, and engage with alumni, industry, government, and community partners through outreach activities. In order to produce engineers prepared for success across a range of career paths, our academic program integrates training in engineering principles, critical thinking, hands-on projects, open-ended problem solving, and the essential skills of teamwork, communication, and ethics.

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Browsing Department of Mechanical Engineering by Author "Acar, Pinar"

Now showing 1 - 4 of 4
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    Deep Reinforcement Learning for Multi-Phase Microstructure Design
    Yang, Jiongzhi; Harish, Srivatsa; Li, Candy; Zhao, Hengduo; Antous, Brittney; Acar, Pinar (2021-03-22)
    This paper presents a de-novo computational design method driven by deep reinforcement learning to achieve reliable predictions and optimum properties for periodic microstructures. With recent developments in 3-D printing, microstructures can have complex geometries and material phases fabricated to achieve targeted mechanical performance. These material property enhancements are promising in improving the mechanical, thermal, and dynamic performance in multiple engineering systems, ranging from energy harvesting applications to spacecraft components. The study investigates a novel and efficient computational framework that integrates deep reinforcement learning algorithms into finite element-based material simulations to quantitatively model and design 3-D printed periodic microstructures. These algorithms focus on improving the mechanical and thermal performance of engineering components by optimizing a microstructural architecture to meet different design requirements. Additionally, the machine learning solutions demonstrated equivalent results to the physics-based simulations while significantly improving the computational time efficiency. The outcomes of the project show promise to the automation of the design and manufacturing of microstructures to enable their fabrication in large quantities with the utilization of the 3-D printing technology.
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    Mathematical Strategies for Design Optimization of Multiphase Materials
    Catania, Rick; Diraz, Abdalla; Maier, Dominic; Tagle, Armani; Acar, Pinar (Hindawi Publishing Corp, 2019-03-12)
    This work addresses various mathematical solution strategies adapted for design optimization of multiphase materials. The goal is to improve the structural performance by optimizing the distribution of multiple phases that constitute the material. Examples include the optimization of multiphase materials and composites with spatially varying fiber paths using a finite element analysis scheme. In the first application, the phase distribution of a two-phase material is optimized to improve the structural performance. A radial basis function (RBF) based machine learning algorithm is utilized to perform a computationally efficient design optimization and it is found to provide equivalent results with the physical model. The second application concentrates on the optimization of spatially varying fiber paths of a composite material. The fiber paths are described by the Non-Uniform Rational Bezier (B)-Spline Surface (NURBS) using a bidirectional control point representation including 25 parameters. The optimum fiber path is obtained for various loading configurations by optimizing the NURBS parameters that control the overall distribution of fibers. Next, a direct sensitivity analysis is conducted to choose the critical set of parameters from the design point to improve the computational time efficiency. The optimized fiber path obtained with the reduced number of NURBS parameters is found to provide similar structural properties compared to the optimized fiber path that is modeled with a full NURBS representation with 25 parameters.
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    Multi-scale computational modeling of lightweight aluminum-lithium alloys
    Acar, Pinar (Elsevier Ltd, 2019-03-07)
    The present study addresses the multi-scale computational modeling of a lightweight Aluminum-Lithium (Al-Li) 2070 alloy. The Al-Li alloys display significant anisotropy in material properties because of their strong crystallographic texture. To understand the relationships between processing, microstructural textures at different material points and tailored material properties, a multi-scale simulation is performed by controlling the texture evolution during deformation. To achieve the multi-scale framework, a crystal plasticity model based on a one-point probability descriptor, Orientation Distribution Function (ODF), is implemented to study the texture evolution. Next, a two-way coupled multi-scale model is developed, where the deformation gradient at the macro-scale integration points is passed to the micro-scale ODF model and the homogenized stress tensor at the micro-scale is passed back to the macro-scale model. A gradient-based optimization scheme which incorporates the multi-scale continuum sensitivity method is utilized to calibrate the slip system parameters of the alloy using the available experimental data. Next, the multi-scale simulations are performed for compression and tension using the calibrated crystal plasticity model, and the texture data is compared to the experiments. With the presented multi-scale modeling scheme, we achieve the location-specific texture predictions for a new generation Al-Li alloy for different deformation processes. © 2019 The Author
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    Uncertainty Quantification for Ti-7Al Alloy Microstructure with an Inverse Analytical Model (AUQLin)
    Acar, Pinar (MDPI, 2019-05-31)
    The present study addresses an inverse problem for observing the microstructural stochasticity given the variations in the macro-scale material properties by developing an analytical uncertainty quantification (UQ) model called AUQLin. The uncertainty in the material property is modeled with the analytical algorithm, and then the uncertainty propagation to the microstructure is solved with an inverse problem that utilizes the transformation of random variables principle. The inverse problem leads to an underdetermined linear system, and thus produces multiple solutions to the statistical features of the microstructure. The final solution is decided by solving an optimization problem which aims to minimize the difference between the computed and experimental statistical parameters of the microstructure. The final result for the computed microstructural uncertainty is found to provide a good match to the experimental microstructure information.