Browsing by Subject "optimization"

Now showing 1 - 10 of 13
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    A Bayesian Approach to Recovering Missing Component Dependence for System Reliability Prediction via Synergy Between Physics and Data
    (ASME, 2021-11) Li, Huiru; Du, Xiaoping; Mechanical and Energy Engineering, School of Engineering and Technology
    Predicting system reliability is often a core task in systems design. System reliability depends on component reliability and dependence of components. Component reliability can be predicted with a physics-based approach if the associated physical models are available. If the models do not exist, component reliability may be estimated from data. When both types of components coexist, their dependence is often unknown, and the component states are therefore assumed independent by the traditional method, which can result in a large error. This work proposes a new system reliability method to recover the missing component dependence, thereby leading to a more accurate estimate of the joint probability density (PDF) of all the component states. The method works for series systems whose load is shared by its components that may fail due to excessive loading. For components without physical models available, the load data are recorded upon failure, and equivalent physical models are created; the model parameters are estimated by the proposed Bayesian approach. Then models of all component states become available, and the dependence of component states, as well as their joint PDF, can be estimated. Four examples are used to evaluate the proposed method, and the results indicate that the proposed method can produce more accurate predictions of system reliability than the traditional method that assumes independent component states.
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    Deep swarm: Nested particle swarm optimization
    (IEEE, 2017-11) Eberhart, Russell C.; Groves, Doyle J.; Woodward, Joshua K.; Electrical and Computer Engineering, School of Engineering and Technology
    A new generation of particle swarm optimization (PSO) has been developed that automatically evolves optimal or near-optimal values for parameters of the PSO algorithm such as population size and neighborhood size, and, if used, parameters of associated neural network(s), such as number of hidden processing elements (PEs). Called Deep Swarm, it is a nested version of PSO, and comprises swarms within a swarm.
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    Energy Optimization of Air Handling Unit Using CO2 Data and Coil Performance
    (ASME, 2016-11) Razban, Ali; Edalatnoor, Arash; Goodman, David; Chen, Jie; Mechanical Engineering, School of Engineering and Technology
    Air handling unit systems (AHU) are the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. To make the system more energy efficient without compromising comfort, it is very important for building energy management personnel to have tools to monitor the system performance and optimize its operation. Models are needed to meet the needs. The objectives of this study were to (1) develop models for the AHU elements and (2) implement control strategies to improve energy efficiency without sacrificing room comfort based on the published American Society of Heating Refrigeration and Air Conditioning Engineers (ASHRAE) standard. In this study, algorithms were developed to model the energy usage for heating/cooling coils as well as fans for AHU. Enthalpy based effectiveness and Dry Wet coil methods were identified and compared for accuracy of evaluating the system performance. Two different types of control systems were modeled and the results were shown based on occupancy reflected by the collected the rooms’ CO2 data. Discrete On/Off and fuzzy logic controller techniques were simulated using Simulink Matlab software and compared based on energy reduction and system performance. The models were used on an AHU in one of the campus buildings. The data for model inputs were collected wirelessly from the building using fully function devices (FFD) and a pan coordinator to send/receive the data. Current building management system Metasys software was also used to get additional data. The AHU modeling was done using Engineering Equation Solver (EES) Software for the coils and subsystems. Moving Average technique was utilized to process the data. The models were validated by comparing the calculated results with these measured experimentally. Simulation results showed that in humid regions, where there is more than 45% of relative humidity, the dry wet coil method is the effective way to provide more accurate details of the heat transfer and energy usage of the AHU comparing to the enthalpy based effectiveness. Also results of fuzzy logic controller method show that 62% of the current return fan energy can be reduced weekly using this method without sacrificing the occupant comfort level comparing to the ON/OFF method. Energy consumption can be optimized inside the building using fuzzy logic controller. At the same time system performance can be increased by taking the appropriate steps to prevent the loss of static pressure in the ducts. The implementation of the method developed in this study will improve the energy efficiency of the AHU while the occupants comfort level stay intact.
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    Hardware Speculation Vulnerabilities and Mitigations
    (IEEE, 2021-10) Swearingen, Nathan; Hosler, Ryan; Zou, Xukai; Computer and Information Science, School of Science
    This paper will discuss speculation vulnerabilities, which arise from hardware speculation, an optimization technique. Unlike many other types of vulnerabilities, these are very difficult to patch completely, and there are techniques developed to mitigate them. We will look at many of the variants of this type of vulnerability. We will look at the techniques mitigating those vulnerabilities and the effectiveness and scope of each. Finally, we will compare and evaluate different vulnerabilities and mitigation techniques and recommend how various mitigation techniques apply to different situations.
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    High-Torque Electric Machines: State of the Art and Comparison
    (MDPI, 2022-07-30) Alibeik, Maryam; dos Santos, Euzeli C.; Electrical and Computer Engineering, School of Engineering and Technology
    The state of the art of high-torque electric motors has been reviewed in this paper. This paper presents a literature review of high-torque density electric machines based on their airgap classifications, which brings a unique consideration to new design ideas to increase torque density. Electric machines are classified into three main groups based on their airgap configuration, i.e., (1) machines with a constant airgap, (2) machines with a variable airgap, and (3) machines with an eccentric airgap. This paper also presents the modeling of a high-torque airgap-less electric motor based on the concept of eccentric airgap. The torque density of this motor has been compared to motors available in the literature review. Among electrical motors with no permanent-magnet, airgap-less electric motors take the lead in terms of torque density, which is almost five times greater than the next motor, “in-wheel for electric vehicle”.
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    Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures
    (ASME, 2016-08) Liu, Kai; Detwiler, Duane; Tovar, Andres; Department of Mechanical Engineering, School of Engineering and Technology
    This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Efficient Global Optimization (EGO) followed by multi-objective design optimization to find the optimal material distribution. The proposed methodology is demonstrated using examples from multiple physics and compared with traditional multimaterial topology optimization method.
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    Multiphase Thermomechanical Topology Optimization of Functionally Graded Lattice Injection Molds
    (ASME, 2016-08) Wu, Tong; Liu, Kai; Tovar, Andres; Department of Mechanical Engineering, School of Engineering and Technology
    This work presents a design methodology of lightweight, thermally efficient injection molds with functionally graded lattice structure using multiphase thermomechanical topology optimization. The aim of this methodology is to increase or maintain thermal and mechanical performance as well as to lower the cost of thermomechanical components such as injection molds when these are fabricated using additive manufacturing technologies. The proposed design approach makes use of thermal and mechanical finite element analyses to evaluate the components stiffness and heat conduction in two length scales: mesoscale and macroscale. The mesoscale contains the structural features of the lattice unit cell. Mesoscale homogenized properties are implemented in the macroscale model, which contains the components boundary conditions including the external mechanical loads as well as the heat sources and heat sinks. The macroscale design problem addressed in this work is to find the optimal distribution of given number of lattice unit cell phases within the component so its mass is minimized, while satisfying stiffness and heat conduction constraints of the overall component and the specific regions. This problem is solved through two steps: conceptual design generation and multiphase material distribution. In the first step, the mass is minimized subject to constraints of mechanical compliance and thermal cost function. In the second step, a given number of lattice material are optimally distributed subjected to nonlinear thermal and mechanical constraints, e.g., maximum nodal temperature, maximum nodal displacement. The proposed design approach is demonstrated through 2D and 3D examples including the optimal design of the core of an injection mold. The results demonstrate that a small reduction in mechanical and thermal performance allows for significant mass savings: the second example shows that 3.5% heat conduction reduction and 8.7% stiffness reduction results in 30.3% mass reduction.
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    Multiphase topology optimization of lattice injection molds
    (Elsevier, 2017-11) Wu, Tong; Liu, Kai; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    This work presents a topology optimization approach for lattice structures subjected to thermal and mechanical loads. The focus of this work is the design of injection molds. The proposed approach seeks to minimize the injection mold mass while satisfying constraints on mechanical and thermal performance. The optimal injection molds are characterized by a quasi-periodic distribution of lattice unit cells of variable relative density. The resulting lattice structures are suitable for additive manufacturing. The proposed structural optimization approach uses thermal and mechanical finite element analyses at two length scales: mesoscale and macroscale. At the mesoscale, lattice unit cells are utilized to obtain homogenized thermal and mechanical properties as a function of the lattice relative density. At the macroscale, the lattice unit cells are optimally distributed using the homogenized properties. The proposed design approach is demonstrated through 2D and 3D examples including the optimal design of an injection mold. The optimized injection mold is prototyped using additive manufacturing. The numerical model of the optimized mold shows that, with respect to a traditional solid mold design, a mass reduction of over 30% can be achieved with a small increase in nodal displacement (under 5 microns) and no difference in nodal temperature.
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    Multiscale Topology Optimization With Gaussian Process Regression Models
    (American Society of Mechanical Engineers, 2021-08-17) Najmon, Joel C.; Valladares, Homero; Tovar, Andres; Mechanical Engineering, School of Engineering and Technology
    Multiscale topology optimization (MSTO) is a numerical design approach to optimally distribute material within coupled design domains at multiple length scales. Due to the substantial computational cost of performing topology optimization at multiple scales, MSTO methods often feature subroutines such as homogenization of parameterized unit cells and inverse homogenization of periodic microstructures. Parameterized unit cells are of great practical use, but limit the design to a pre-selected cell shape. On the other hand, inverse homogenization provide a physical representation of an optimal periodic microstructure at every discrete location, but do not necessarily embody a manufacturable structure. To address these limitations, this paper introduces a Gaussian process regression model-assisted MSTO method that features the optimal distribution of material at the macroscale and topology optimization of a manufacturable microscale structure. In the proposed approach, a macroscale optimization problem is solved using a gradient-based optimizer The design variables are defined as the homogenized stiffness tensors of the microscale topologies. As such, analytical sensitivity is not possible so the sensitivity coefficients are approximated using finite differences after each microscale topology is optimized. The computational cost of optimizing each microstructure is dramatically reduced by using Gaussian process regression models to approximate the homogenized stiffness tensor. The capability of the proposed MSTO method is demonstrated with two three-dimensional numerical examples. The correlation of the Gaussian process regression models are presented along with the final multiscale topologies for the two examples: a cantilever beam and a 3-point bending beam.
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    Optimal Design for Deployable Structures Using Origami Tessellations
    (ASME, 2020-01) Cardona, Carolina; Tovar, Andres; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
    This work presents innovative origami optimization methods for the design of unit cells for complex origami tessellations that can be utilized for the design of deployable structures. The design method used to create origami tiles utilizes the principles of discrete topology optimization for ground structures applied to origami crease patterns. The initial design space shows all possible creases and is given the desired input and output forces. Taking into account foldability constraints derived from Maekawa’s and Kawasaki’s theorems, the algorithm designates creases as active or passive. Geometric constraints are defined from the target 3D object. The periodic reproduction of this unit cell allows us to create tessellations that are used in the creation of deployable shelters. Design requirements for structurally sound tessellations are discussed and used to evaluate the effectiveness of our results. Future work includes the applications of unit cells and tessellation design for origami inspired mechanisms. Special focus will be given to self-deployable structures, including shelters for natural disasters.