Browsing by Subject "Optimization"

Now showing 1 - 10 of 16
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    Applying Machine Learning to Optimize Sintered Powder Microstructures from Phase Field Modeling
    (2020-12) Batabyal, Arunabha; Zhang, Jing; Yang, Shengfeng; Du, Xiaoping
    Sintering is a primary particulate manufacturing technology to provide densification and strength for ceramics and many metals. A persistent problem in this manufacturing technology has been to maintain the quality of the manufactured parts. This can be attributed to the various sources of uncertainty present during the manufacturing process. In this work, a two-particle phase-field model has been analyzed which simulates microstructure evolution during the solid-state sintering process. The sources of uncertainty have been considered as the two input parameters surface diffusivity and inter-particle distance. The response quantity of interest (QOI) has been selected as the size of the neck region that develops between the two particles. Two different cases with equal and unequal sized particles were studied. It was observed that the neck size increased with increasing surface diffusivity and decreased with increasing inter-particle distance irrespective of particle size. Sensitivity analysis found that the inter-particle distance has more influence on variation in neck size than that of surface diffusivity. The machine-learning algorithm Gaussian Process Regression was used to create the surrogate model of the QOI. Bayesian Optimization method was used to find optimal values of the input parameters. For equal-sized particles, optimization using Probability of Improvement provided optimal values of surface diffusivity and inter-particle distance as 23.8268 and 40.0001, respectively. The Expected Improvement as an acquisition function gave optimal values 23.9874 and 40.7428, respectively. For unequal sized particles, optimal design values from Probability of Improvement were 23.9700 and 33.3005 for surface diffusivity and inter-particle distance, respectively, while those from Expected Improvement were 23.9893 and 33.9627. The optimization results from the two different acquisition functions seemed to be in good agreement with each other. The results also validated the fact that surface diffusivity should be higher and inter-particle distance should be lower for achieving larger neck size and better mechanical properties of the material.
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    Asset allocation in frequency and in 3 spatial dimensions for electronic warfare application
    (2016-04) Crespo, Jonah Greenfield; Christopher, Lauren Ann; Dos Santos, Euzeli Cipriano, Jr.; Rizkalla, Maher; Li, Lingxi; King, Brian
    This paper describes two research areas applied to Particle Swarm Optimization (PSO) in an electronic warfare asset scenario. First, a three spatial dimension solution utilizing topographical data is implemented and tested against a two dimensional solution. A three dimensional (3D) optimization increases solution space for optimization of asset location. Topography from NASA's Digital Elevation Model is also added to the solution to provide a realistic scenario. The optimization is tested for run time, average distances between receivers, average distance between receivers and paired transmitters, and transmission power. Due to load times of maps and increased iterations, the average run times were increased from 123ms to 178ms, which remains below the 1 second target for convergence speeds. The spread distance between receivers was able to increase from 86km to 89km. The distance between receiver and its paired transmitters as well as the total received power did not change signi cannily. In the second research contribution, a user input is created and placed into an unconstrained 2D active swarm. This \human in the swarm" scenario allows a user to change keep-away boundaries during optimization. The blended human and swarm solution successfully implemented human input into a running optimization with a time delay. The results of this research show that a electronic warfare solutions with real 3D topography can be simulated with minimal computational costs over two dimensional solutions and that electronic warfare solutions can successfully optimize using human input data.
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    Bayesian Optimization of Active Materials for Lithium-Ion Batteries
    (SAE, 2021-04) Valladares, Homero; Li, Tianyi; Zhu, Likun; El-Mounayri, Hazim; Tovar, Andres; Hashem, Ahmed; Abdel-Ghany, Ashraf E.; Mechanical Engineering, School of Engineering and Technology
    The design of better active materials for lithium-ion batteries (LIBs) is crucial to satisfy the increasing demand of high performance batteries for portable electronics and electric vehicles. Currently, the development of new active materials is driven by physical experimentation and the designer’s intuition and expertise. During the development process, the designer interprets the experimental data to decide the next composition of the active material to be tested. After several trial-and-error iterations of data analysis and testing, promising active materials are discovered but after long development times (months or even years) and the evaluation of a large number of experiments. Bayesian global optimization (BGO) is an appealing alternative for the design of active materials for LIBs. BGO is a gradient-free optimization methodology to solve design problems that involve expensive black-box functions. An example of a black-box function is the prediction of the cycle life of LIBs. The cycle life cannot be predicted using a simple closed-form expression but only through the cycling performance test or a numerical simulation. BGO has two main components: a surrogate probabilistic model of the black-box function and an acquisition function that guides the optimization. This research employs BGO in the design of cathode active materials for LIB cells. The training data corresponds to the initial capacity and cycle life of five coin cells with different compositions of LiNixMn2 − xO4 in their cathode, where x is the content of Ni. BGO utilizes the experimental data to identify five new compositions that can produce cells with high initial capacity and\or large cycle life. The surrogate models of the initial capacity and cycle life are Gaussian Processes. The acquisition function is the constrained multi-objective expected improvement. The results show that BGO can identify high-performance active materials for LIBs. Designers can use the data generated during the optimization to decide the composition of the next batch of active materials to be tested, i.e., guide the physical experimentation.
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    Charge optimization of lithium-ion batteries for electric-vehicle application
    (2015-03-02) Pramanik, Sourav; Anwar, Sohel; Wasfy, Tamar; Li, Lingxi
    In recent years Lithium-Ion battery as an alternate energy source has gathered lot of importance in all forms of energy requiring applications. Due to its overwhelming benefits over a few disadvantages Lithium Ion is more sought of than any other Battery types. Any battery pack alone cannot perform or achieve its maximum capacity unless there is some robust, efficient and advanced controls developed around it. This control strategy is called Battery Management System or BMS. Most BMS performs the following activity if not all Battery Health Monitoring, Temperature Monitoring, Regeneration Tactics, Discharge Profiles, History logging, etc. One of the major key contributor in a better BMS design and subsequently maintaining a better battery performance and EUL is Regeneration Tactics. In this work, emphasis is laid on understanding the prevalent methods of regeneration and designing a new strategy that better suits the battery performance. A performance index is chosen which aims at minimizing the effort of regeneration along with a minimum deviation from the rated maximum thresholds for cell temperature and regeneration current. Tuning capability is provided for both temperature deviation and current deviation so that it can be tuned based on requirement and battery chemistry and parameters. To solve the optimization problem, Pontryagin's principle is used which is very effective for constraint optimization with both state and input constraints. Simulation results with different sets of tuning shows that the proposed method has a lot of potential and is capable of introducing a new dynamic regeneration tactic for Lithium Ion cells. With the current optimistic results from this work, it is strongly recommended to bring in more battery constraints into the optimization boundary to better understand and incorporate battery chemistry into the regeneration process.
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    Concurrent topology optimization of structures and materials
    (2013-12-11) Liu, Kai; Tovar, Andrés; Nematollahi, Khosrow; Koskie, Sarah; Anwar, Sohel
    Topology optimization allows designers to obtain lightweight structures considering the binary distribution of a solid material. The introduction of cellular material models in topology optimization allows designers to achieve significant weight reductions in structural applications. However, the traditional topology optimization method is challenged by the use of cellular materials. Furthermore, increased material savings and performance can be achieved if the material and the structure topologies are concurrently designed. Hence, multi-scale topology optimization methodologies are introduced to fulfill this goal. The objective of this investigation is to discuss and compare the design methodologies to obtaining optimal macro-scale structures and the corresponding optimal meso-scale material designs in continuum design domains. These approaches make use of homogenization theory to establish communication bridges between both material and structural scales. The periodicity constraint makes such cellular materials manufacturable while relaxing the periodicity constraint to achieve major improvements of structural performance. Penalization methods are used to obtain binary solutions in both scales. The proposed methodologies are demonstrated in the design of stiff structure and compliant mechanism synthesis. The multiscale results are compared with the traditional structural-level designs in the context of Pareto solutions, demonstrating benefits of ultra-lightweight configurations. Errors involved in the mult-scale topology optimization procedure are also discussed. Errors are mainly classified as mesh refinement errors and homogenization errors. Comparisons between the multi-level designs and uni-level designs of solid structures, structures using periodic cellular materials and non-periodic cellular materials are provided. Error quantifications also indicate the superiority of using non-periodic cellular materials rather than periodic cellular materials.
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    Design and Fatigue Analysis of an LWD Drill Tool
    (2019-08) Joshi, Riddhi; El-Mounayri, Hazim; Tovar, Andres; Nematollahi, Khosrow
    Previous works suggest that 80% to 90% of failures observed in the rotary machines are accounted for fatigue failure. And it is observed that cyclic stresses are more critical than steady stresses when the failure occurred is due to fatigue. One of the most expensive industries involving rotary machines is the Oil and Gas industry. The large drilling tools are used for oil extracts on-shore and off-shore. There are several forces that act on a drilling tool while operating below the earth's surface. Those forces are namely pressure, bending moment and torque. The tool is designed from the baseline model of the former tool in Solidworks and Design Molder. Here load acting due to pressure and torque accounts for steady stress i.e., Mean Stress and loading acting due to bending moment account for fluctuating stress i.e., Alternating Stress. The loading and boundary conditions have been adapted from Halliburton’s previous works for the LWD drill tool to better estimate the size of the largest possible transducer. The fatigue analysis of static load cases is carried out in Ansys Mechanical Workbench 19.0 using static structural analysis. The simulation is run to obtain results for total deformation, equivalent stress, and user-defined results. The component is designed for infinite life to calculate the endurance limit. Shigley guidelines and FKM guidelines are compared as a part of a study to select the best possible approach in the current application. The width of the imaging pocket is varied from 1.25 inches to 2.0 inches to accommodate the largest possible transducer without compromising the structural integrity of the tool. The optimum design is chosen based on the stress life theory criteria namely Gerber theory and Goodman Theory.
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    A genetic algorithm approach to best scenarios selection for performance evaluation of vehicle active safety systems
    (2015) Gholamjafari, Ali; Li, Lingxi
    Gholamjafari, Ali MSECE, Purdue University, May 2015. A Genetic Algorithm Approach to Best Scenarios Selection for Performance Evaluation of Vehicle Active Safety Systems . Major Professor: Dr. Lingxi Li. One of the most crucial tasks for Intelligent Transportation Systems is to enhance driving safety. During the past several years, active safety systems have been broadly studied and they have been playing a significant role in vehicular safety. Pedestrian Pre- Collision System (PCS) is a type of active safety systems which is used toward pedestrian safety. Such system utilizes camera, radar or a combination of both to detect the relative position of the pedestrians towards the vehicle. Based on the speed and direction of the car, position of the pedestrian, and other useful information, the systems can anticipate the collision/near-collision events and take proper actions to reduce the damage due to the potential accidents. The actions could be triggering the braking system to stop the car automatically or could be simply sending a warning signal to the driver depending on the type of the events. We need to design proper testing scenarios, perform the vehicle testing, collect and analyze data to evaluate the performance of PCS systems. It is impossible though to test all possible accident scenarios due to the high cost of the experiments and the time limit. Therefore, a subset of complete testing scenarios (which is critical due to the different types of cost such as fatalities, social costs, the numbers of crashes, etc.) need to be considered instead. Note that selecting a subset of testing scenarios is equivalent to an optimization problem which is maximizing a cost function while satisfying a set of constraints. In this thesis, we develop an approach based on Genetic Algorithm to solve such optimization problems. We then utilize crash and field database to validate the accuracy of our algorithm. We show that our method is effective and robust, and runs much faster than exhaustive search algorithms. We also present some crucial testing scenarios as the result of our approach, which can be used in PCS field testing.
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    Identification of unknown petri net structures from growing observation sequences
    (2015-06-08) Ruan, Keyu; Li, Lingxi; King, Brian; Chien, Stanley Yung-Ping
    This thesis proposed an algorithm that can find optimized Petri nets from given observation sequences according to some rules of optimization. The basic idea of this algorithm is that although the length of the observation sequences can keep growing, we can think of the growing as periodic and algorithm deals with fixed observations at different time. And the algorithm developed has polynomial complexity. A segment of example code programed according to this algorithm has also been shown. Furthermore, we modify this algorithm and it can check whether a Petri net could fit the observation sequences after several steps. The modified algorithm could work in constant time. These algorithms could be used in optimization of the control systems and communication networks to simplify their structures.
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    Measuring success: perspectives from three optimization programs on assessing impact in the age of burnout
    (Oxford University Press, 2020-12) Lourie, Eli M.; Stevens, Lindsay A.; Webber, Emily C.; Pediatrics, School of Medicine
    Electronic health record (EHR) optimization has been identified as a best practice to reduce burnout and improve user satisfaction; however, measuring success can be challenging. The goal of this manuscript is to describe the limitations of measuring optimizations and opportunities to combine assessments for a more comprehensive evaluation of optimization outcomes. The authors review lessons from 3 U.S. healthcare institutions that presented their experiences and recommendations at the American Medical Informatics Association 2020 Clinical Informatics conference, describing uses and limitations of vendor time-based reports and surveys utilized in optimization programs. Compiling optimization outcomes supports a multi-faceted approach that can produce assessments even as time-based reports and technology change. The authors recommend that objective measures of optimization must be combined with provider and clinician-defined value to provide long term improvements in user satisfaction and reduce EHR-related burnout.
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    Nonlinear Constrained Component Optimization of a Plug-in Hybrid Electric Vehicle
    (2010-12) Yildiz, Emrah Tolga; Anwar, Sohel; Chen, Yaobin; Izadian, Afshin
    Today transportation is one of the rapidly evolving technologies in the world. With the stringent mandatory emission regulations and high fuel prices, researchers and manufacturers are ever increasingly pushed to the frontiers of research in pursuit of alternative propulsion systems. Electrically propelled vehicles are one of the most promising solutions among all the other alternatives, as far as; reliability, availability, feasibility and safety issues are concerned. However, the shortcomings of a fully electric vehicle in fulfilling all performance requirements make the electrification of the conventional engine powered vehicles in the form of a plug-in hybrid electric vehicle (PHEV) the most feasible propulsion systems. The optimal combination of the properly sized components such as internal combustion engine, electric motor, energy storage unit are crucial for the vehicle to meet the performance requirements, improve fuel efficiency, reduce emissions, and cost effectiveness. In this thesis an application of Particle Swarm Optimization (PSO) approach to optimally size the vehicle powertrain components (e.g. engine power, electric motor power, and battery energy capacity) while meeting all the critical performance requirements, such as acceleration, grade and maximum speed is studied. Compared to conventional optimization methods, PSO handles the nonlinear constrained optimization problems more efficiently and precisely. The PHEV powertrain configuration with the determined sizes of the components has been used in a new vehicle model in PSAT (Powertrain System Analysis Toolkit) platform. The simulation results show that with the optimized component sizes of the PHEV vehicle (via PSO), the performance and the fuel efficiency of the vehicle are significantly improved. The optimal solution of the component sizes found in this research increased the performance and the fuel efficiency of the vehicle. Furthermore, after reaching the desired values of the component sizes that meet all the performance requirements, the overall emission of hazardous pollutants from the PHEV powertrain is included in the optimization problem in order to obtain updated PHEV component sizes that would also meet additional design specifications and requirements.