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Power System and Green Energy Conference (PSGEC)

作者： Yu, Huiqun Shuai, Yongsheng Peng, Daogang Jin, Donghui Shanghai Univ Elect Power Coll Automat Engn Shanghai Peoples R China

ISBN: (纸本)9781728190952

With the growing penetration of renewable energy sources such as wind and solar in the grid, intermittence and volatility bring many problems to the power grid, which makes the power grid lack of frequency modulation capacity and poor effect of frequency regulation. In this situation, the traditional automatic generation control (AGC), as one of the main measures of power grid frequency control, is not enough to meet the challenge of current frequency regulation. Therefore, this research proposes a new optimal control strategy. Firstly, in view of the comparative evaluation of the technical traits of conventional units and energy storage units participating in frequency modulation of grid, the frequency regulation model of energy storage combined units is established. Then, based on this, a two-level control strategy is proposed. The upper level takes economy and state of charge (SOC) as constraints, and the power is allocated based on multi-objective genetic algorithm. The lower level uses model predictive control to realize frequency optimization control. Finally, the simulation effects exhibit the effectiveness of the proposed manipulate method on MATLAB/Simulink platform.

关键词： automatic generation control frequency regulation multi objective genetic algorithm model predictive control

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Health Recommender System for Cervical Cancer Prognosis in Women 6

Health Recommender System for Cervical Cancer Prognosis in W...

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6th International Conference on Inventive Computation Technologies (ICICT)

作者： Kuanr, Madhusree Mohapatra, Puspanjali Piri, Jayshree IIIT Bhubaneswar Dept CSE Bhubaneswar India

ISBN: (纸本)9781728185019

The large amount of digital data of patients are available for health domain to be used successfully for extracting information and aid disease prediction. Therefore, the available digital information for patient-oriented decision-making substantially expanded. Recommender systems may offer more laymen-friendly knowledge to patients in this sense, helping to better understand their clinical status as reflected by their reports. Health Recommender Systems (HRSs) are a viable solution when it comes to offering resources to support clinicians in the diagnosis of illnesses, as well as supporting people with guidance on how to preserve their health. This study has proposed a Health Recommender System using a feature selection method based on the multi objective genetic algorithm (MOGA) to help women by providing information on the features responsible for prognosis of Cervical Cancer in women. It also recommends some prediction models for Cervical Cancer prediction with high accuracy. It has used Cervical Cancer Risk Classification dataset for implementation and accuracy as the evaluation parameter.

关键词： Health Recommender System multi objective genetic algorithm Feature Selection Prediction Accuracy

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Mixed-Flow Assembly Line Balancing with Uncertain Assembly times in Remanufacturing

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IFAC-PapersOnLine 2022年第10期55卷 97-102页

作者： Qingtao Liu Jiayao Song Jingxiang Lv Ou Tang Zeyu Zhang Key Laboratory of Road Construction Technology and Equipment of MOE Chang'an University 710064 Xi'an China Division of Production Economics Department of Management and Engineering Linköping University SE-581 83 Linköping Sweden

Remanufacturing has in recent decades become an alternative way for sustainable development to cope with the increasingly resource crisis and environmental pollution problems. The line balancing of remanufacturing is more complex than that of traditional manufacturing due to the uncertainty in the remanufacturing process, especially as the assembly time is uncertain. As a result, the line balancing of the remanufacturing assembly has become a critical issue. This paper evaluates the uncertain assembly time in each station based on an approach of Fuzzy Graphical Evaluation and Review Technology (FGERT) network. A remanufacturing multi-objective mixed-flow assembly model is established by optimizing the cycle time, assembly line smoothing coefficient and balance time loss rate. Then an adaptive bilayer genetic algorithm is proposed to solve the optimization problem. Finally, an example was given to demonstrate the effectiveness of the proposed method. Results show that the production rhythm balance of remanufacturing assembly is significantly improved, and the idle time in each assembly station has been reduced as well. The cycle time of the remanufacturing has reduced from 344s to 336s, with the smoothing coefficient and balance time loss rate of being 8.73 and 1.69% compared to that without assembly line balancing. The proposed method provides a useful tool for improving mixed-flow remanufacturing assembly lines in an electromechanics industry.

关键词： Remanufacturing Mixed-model assembly line Assembly line balancing multi objective genetic algorithm

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genetic-algorithm-Based Design for Rideshare and Heterogeneous Constellations

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第5期57卷 1021-1032页

作者： Wagner, Katherine M. Black, Jonathan T. Virginia Polytech Inst & State Univ Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

Responsive and resilient space-based systems are needed to satisfy changing mission requirements and react to unforeseen challenges. Nontraditional constellations, such as heterogeneous and rideshare constellations, have the potential to increase resiliency and responsiveness, as can using commercial off-the-shelf parts. However, these constellation types introduce design challenges that cannot be solved by analytical techniques. A novel methodology for optimizing these constellations using a multi-objective genetic algorithm and model-based systems engineering techniques is introduced. Included in this methodology are new techniques for measuring constellation resiliency and rapidly determining reachability between two orbits. The methodology is applied to a validation case of designing a Mars-orbiting position, navigation, timing, and communications constellation. The methodology is then used to examine the viability of using rideshare opportunities to construct a satellite constellation. The constellation must provide regional imaging coverage in the event of a natural disaster, and its efficacy is evaluated both in the nominal case and in the case when multiple satellites have failed.

关键词： multi objective genetic algorithm Constellations Satellite Constellations Position, Navigation, and Timing Commercial off the Shelf Model Based System Engineering Propellant Orbital Elements Ground Station Disaggregated Integral System Concept Optimization

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Two-Phase Neural Combinatorial Optimization with Reinforcement Learning for Agile Satellite Scheduling

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JOURNAL OF AEROSPACE INFORMATION SYSTEMS 2020年第7期17卷 346-357页

作者： Zhao, Xuexuan Wang, Zhaokui Zheng, Gangtie Tsinghua Univ Sch Aerosp Engn Beijing 100084 Peoples R China

The agile Earth observing satellite (AEOS) scheduling problem consists of scheduling a set of image acquisitions and selecting the start time of each acquisition, satisfying the constraints and maximizing the gain. With the development of satellite technologies and higher requirements of users, the AEOS scheduling problem has to consider constraints and criteria that vary with the run-time on-orbit state, such as the transition time constraint and image quality criteria. These requirements increase the difficulty to model the problem. In this paper, a two-phase neural combinatorial optimization method with reinforcement learning is proposed for the AEOS scheduling problem. First, a neural combinatorial optimization with the reinforcement learning method is proposed to select a set of possible acquisitions and provide a permutation of them. Second, with the selected acquisition sequence, a reinforcement learning algorithm based on deep deterministic policy gradient is proposed to select the start time of each acquisition under the time constraints. The experimental results with artificial instances show that the proposed method is a viable candidate for the highly combinatorial AEOS scheduling problem;furthermore, it has the ability to cope with the run-time assessment, like the transition time calculation and the image quality evaluation.

关键词： Reinforcement Learning Satellite Constellations Earth Satellite Technology Control Moment Gyroscope Recurrent Neural Network multi objective genetic algorithm EOS Probability Distribution Stochastic Gradient Descent

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Fast multi-objective Aerodynamic Optimization Using Sequential Domain Patching and multifidelity Models

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JOURNAL OF AIRCRAFT 2020年第3期57卷 388-398页

作者： Amrit, Anand Leifsson, Leifur Koziel, Slawomir Iowa State Univ Dept Aerosp Engn 537 Bissel Rd Ames IA 50011 USA Reykjavik Univ Engn Optimizat & Modeling Ctr Sch Sci & Engn Menntavegur 1 IS-102 Reykjavik Iceland

Exploration of design tradeoffs for aerodynamic surfaces requires solving of multi-objective optimization (MOO) problems. The major bottleneck here is the time-consuming evaluations of the computational fluid dynamics (CFD) model used to capture the nonlinear physics involved in designing aerodynamic surfaces. This, in conjunction with a large number of simulations necessary to yield a set of designs representing the best possible tradeoffs between conflicting objectives (referred to as a Pareto front), makes CFD-driven MOO very challenging. This paper presents a computationally efficient methodology aimed at expediting the MOO process for aerodynamic design problems. The extreme points of the Pareto front are obtained quickly using single-objective optimizations. Starting from these extreme points, identification of an initial set of Pareto-optimal designs is carried out using a sequential domain patching algorithm. Refinement of the Pareto front, originally obtained at the level of the low-fidelity CFD model, is carried out using local response surface approximations and adaptive corrections. The proposed algorithm is validated using a few multi-objective analytical problems and an aerodynamic problem involving MOO of two-dimensional transonic airfoil shapes where the figures of interest are the drag and pitching moment coefficients. A multifidelity model is constructed using CFD model and control points parameterizing the shape of the airfoil. The results demonstrate that an entire or a part of the Pareto front can be obtained at a low cost when considering up to eight design variables.

关键词： Aerodynamic Shape Optimization Morphing Airfoil CFD Simulation Surrogate Model Reynolds Averaged Navier Stokes multi objective Evolutionary algorithms High Performance Computing multi objective genetic algorithm Sequential Quadratic Programming Lift Coefficient

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Q-Law Aided Direct Trajectory Optimization of Many-Revolution Low-Thrust Transfers

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JOURNAL OF SPACECRAFT AND ROCKETS 2020年第4期57卷 672-682页

作者： Shannon, Jackson L. Ozimek, Martin T. Atchison, Justin A. Hartzell, Christine M. Univ Maryland Dept Aerosp Engn College Pk MD 20740 USA Johns Hopkins Univ Appl Phys Lab Space Explorat Sect 11101 Johns Hopkins Rd Laurel MD 20723 USA

Low-thrust spacecraft trajectory optimization for the many-revolution orbital transfer problem is especially challenging due to the high problem dimension and perturbing accelerations that prevent or complicate accurate analytical solutions. This analysis seeks to simplify the calculation of the nonaveraged spiral trajectory through the use of the well-known Q-Law guidance algorithm. Here, Q-Law is used to seed phases of the direct trajectory optimization problem. We explore the trade space between the fast calculation speed of Q-Law and the time or mass optimality of the full optimization problem using the common example of a geostationary transfer orbit to geostationary transfer. We find that a computationally efficient, near-optimal solution can be achieved by using Q-Law for an initial fraction of a spiral trajectory and direct optimization for the remainder of the trajectory. This reduces the direct optimization problem to a lower dimension and enables the spacecraft to reach a specific final state. Q-Law is particularly effective for portions of the trajectory where there are many eclipses, which can challenge direct optimization methods.

关键词： Trajectory Optimization Thrust Partial Eclipse Geostationary Transfer Orbit Orbital Transfer Guidance algorithms Spacecraft Trajectory Optimization Nonlinear Programming Propellant multi objective genetic algorithm

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Structural Mass Optimization with Manifest Packaging, and Outer Mold Line Updates of a Rigid Mid Lift-to-Drag Mars Entry Lander Vehicle

Structural Mass Optimization with Manifest Packaging, and Ou...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Calderon, Damien T. Sostaric, Ronald R. Garcia, Joseph A. Bowles, Jeffrey, V Kinney, Dave J. Gaytan, Carlos Newton, Holly Amar, Joseph Wiens, Zachary NASA Johnson Space Ctr Struct Engn Div Houston TX 77058 USA NASA Johnson Space Ctr Aerosp & Flight Mech Div Houston TX 77058 USA NASA Ames Res Ctr Syst Anal Off Aeronaut Directorate Mountain View CA 94035 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Over the past four years, NASA has directed the Entry, Descent and Landing Architecture Study (EDLAS) to evaluate candidate technologies to deliver human-scale vehicles (carrying 20t payloads) to the surface of Mars. The study focused on two vehicles in Phase 3 (during 2018 and 2019), one low and one mid lift-to-drag (L/D) vehicle. This paper summarizes updates to the structural and aeroshell outer mold line design for the mid-L/D Mars entry lander vehicle that occurred during EDLAS Phase 3. The design efforts used parametric optimization to arrive at a solution for total vehicle primary and secondary structure mass, and vehicle center-of-gravity location. Additionally, the end of Phase 2 provided an updated surface payload manifest that required three 22 t landers. This paper also summarizes the results of the packaging feasibility study of the updated payload configurations for those three reference missions. The study verified an arrangement of the payload elements within the mid-L/D vehicle that satisfied the center of gravity location limitations for flight.

关键词： Entry, Descent and Landing National Aeronautics and Space Administration Aeroshells Mars Ascent Vehicle Finite Element Analysis Reynolds Averaged Navier Stokes Aerodynamic Stability In Situ Resource Utilization Landing Gear multi objective genetic algorithm

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Biaxial Fatigue Damage in Quasi Isotropic Laminates

Biaxial Fatigue Damage in Quasi Isotropic Laminates

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Skinner, Travis Datta, Siddhant Chattopadhyay, Aditi Hall, Asha Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85287 USA Vehicle Technol Directorate Us Army Res Lab Aberdeen Proving Ground MD 21005 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

An investigation into the biaxial fatigue damage mechanisms in quasi isotropic carbon fiber reinforced polymer (CFRP) composite laminates has been conducted. The goal is to capture early stage damage and obtain an improved understanding of the physics of damage and failure under complex multiaxial loading in anisotropic laminates. Specimen geometry is designed using an optimization technique and digital image correlation (DIC) is performed during static biaxial load tests to validate specimen design. To understand the propagation of damage from the micro- to the macroscale, the fractured surfaces are analyzed during various stages of fatigue using scanning electron microscope (SEM) assisted fractography, and a high-resolution camera is used to capture real time macroscale damage. Specimen stiffness degradation is measured and correlated to the micro- and macroscale damage mechanisms and the biaxial fatigue loading parameters. The results provide critical understanding of the initiation and propagation of damage mechanisms in quasi isotropic laminates under biaxial fatigue.

关键词： Carbon Fiber Reinforced Polymers Scanning Electron Microscope Crack Growth Fatigue Life Fatigue Test ANSYS multi objective genetic algorithm Tsai Wu Failure Criterion Material Properties Fatigue Analysis

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An Open-Source Aerodynamic Framework for Benchmarking multi-Fidelity Methods

An Open-Source Aerodynamic Framework for Benchmarking Multi-...

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AIAA Aviation Forum

作者： Quagliarella, Domenico Diez, Matteo Italian Aerosp Res Ctr CIRA Capua Italy CNR CNR INM Inst Marine Engn Rome Italy

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

This paper presents an open-source aerodynamic framework specifically designed for the assessment and comparison of multi-fidelity methods. The open-source software modules used here were previously assembled for benchmarking uncertainty quantification methods [1] within the activities of NATO AVT-252 (Stochastic Design Optimization for Naval and Aero Military Vehicles). This work presents an extension to multi-fidelity approaches, embedding different physical modeling and discretization levels. The framework is designed to provide the community with an open-source tool for benchmarking multi-fidelity methods within a reasonable computational effort. The software modules are integrated and interfaced so as multi-fidelity analyses can be called automatically and include an airfoil shape parameterization tool, an automatic meshing tool, and aerodynamic solvers. The work is performed in collaboration with NATO AVT-331 (Goal-Driven, multi-Fidelity Approaches for Military Vehicle System-Level Design), where the proposed framework is used for the shape optimization of an RAE-2822 airfoil under transonic conditions.

关键词： NACA 2412 Open Source Software Shape Optimization Aero Military Vehicles Aerodynamic Performance multi objective genetic algorithm Aerodynamic Characteristics XFoil Boundary Layer Transition Potential Flow

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