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13th European-Alliance-for-Innovation (EAI) International Conference on Simulation Tools and Techniques (EAI SIMUtools)

作者： Cai, Wansheng Song, Xi Liu, Chuan Jiang, Dingde Huo, Liuwei State Grid Elect Power Res Inst Nanjing 210003 Peoples R China State Grid Gansu Elect Power CORP Lanzhou 730000 Peoples R China Global Energy Interconnect Res Inst Co Ltd Nanjing 210003 Peoples R China Univ Elect Sci & Technol China Sch Astronaut & Aeronaut Chengdu 611731 Peoples R China

ISBN: (纸本)9783030971243;9783030971236

The Internet of Things (IoT) is a worldwide information network that connects thousands of technological gadgets. We incorporate the SDN network architecture into IoT networks and investigate the characteristics of SDN-based IoT networks in order to make the IoT more flexible and extendable. SDN (Software Defined Networking) is a logical control center with a centralized control plane that makes network management more flexible and efficient. For IoT network management, fine-grained and reliable traffic information is critical. Then, in SDN-based IoT networks, we construct a network traffic model by analyzing the self-similarity of network traffic in IoT network. Then, we collect some traffic statistics in OpenFlow-based switches as the source data and use it to train the proposed network traffic estimation model. Using the measured network traffic in the IoT network, we use the Kalman Filtering to measure and estimate each flow, this scheme just increases a little overhead. Then, we propose to an algorithm to search the more accuracy of traffic. Finally, we run additional simulations to ensure that the suggested measuring system is accurate. Simulation findings suggest that using intelligent optimization approaches, we can improve the granularity and accuracy of traffic data.

关键词： Internet of Things Software Defined Network optimization algorithm Network measurement

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Design optimization Studies of Partially Superconducting Machines based on NASA's High Efficiency Megawatt Motor

Design Optimization Studies of Partially Superconducting Mac...

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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Tallerico, Thomas F. Anderson, Aaron D. Scheidler, Justin J. NASA Glenn Res Ctr Rotating & Drive Syst Branch Cleveland OH 44135 USA NASA Glenn Res Ctr Diagnost & Electromagnet Branch Cleveland OH 44135 USA

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

Single aisle class electric aircraft require high power density and efficient megawatt-scale electric machines to be competitive with their traditional turbofan counterparts. Superconducting machines are seen as a key enabling technology for achieving the electric motor power density and efficiency needed by these aircraft. NASA's High Efficiency Megawatt Motor (HEMM) is a partially superconducting machine being developed at NASA Glenn Research Center as a technology demonstration of a practical near-term superconducting machine. HEMM is being developed to meet the requirements of the generators on NASA's STARC-ABL reference aircraft. HEMM is expected to achieve greater than 16 kW/kg electromagnetic specific power and greater than 98% efficiency at a nominal operating condition of IA MW and 6800 RPM. In this paper, a design optimization algorithm for partially superconducting machines based on HEMM's technologies is used to explore the possible performance of HEMM technology at other machine power levels and operating conditions. The design optimization algorithm is detailed in full, and results for achievable geared and direct drive machine performance are presented.

关键词： High Efficiency Megawatt Motor Hybrid Electric Aircraft optimization algorithm NASA Glenn Research Center Turbofan Finite Element Analysis Superconductor Thermal Performance Biot Savart Law Eddy Current

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Trajectory optimization of Unmanned Aerial Vehicles for Wireless Coverage under Time Constraint

Trajectory Optimization of Unmanned Aerial Vehicles for Wire...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Daniel, Adam T. Wu, Yufei Wang, Zhenbo Gan, Hao Univ Tennessee Tickle Coll Engn Knoxville TN 37996 USA Univ Tennessee Inst Agr Dept Biosyst Engn & Soil Sci Knoxville TN 37996 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

In this paper, we consider the problem of planning the path of an unmanned aerial vehicle (UAV) flying in an area with set boundary and a given number of ground users. The purpose of the UAV is to provide a wireless connection to the ground users. The UAV has set beginning and end locations, as well as a time constraint to perform its task. From these specifications, we formulate an optimal control problem that seeks to maximize the connectivity between the UAV and the ground users. Two cases are considered for this problem: one is a two-dimensional case where the UAV must fly at a fixed height, and the other one is a three-dimensional case where the UAV is free to move in all three spatial dimensions. A pseudospectral optimal control approach is implemented to solve these problems. The results have shown that the pseudospectral optimal control method can generate the trajectories for the UAV to provide the optimal wireless coverage during its mission.

关键词： Pseudospectral Optimal Control Unmanned Aerial Vehicle Nonlinear Programming Lagrange Multipliers Pseudospectral Methods optimization algorithm Energy Efficiency Heading Angle

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Active Manifold and Model Reduction for Multidisciplinary Analysis and optimization

Active Manifold and Model Reduction for Multidisciplinary An...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Boncoraglio, Gabriele Farhat, Charbel Stanford Univ Dept Aeronaut & Astronaut Durand BldgRoom 2242 Stanford CA 94305 USA Stanford Univ Dept Aeronaut & Astronaut Aircraft Struct Durand BldgRoom 257 Stanford CA 94305 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

A computational framework is proposed for efficiently solving multidisciplinary analysis and optimization (MDAO) problems in a relatively high-dimensional design space. It relies on the appropriate blend of linear and nonlinear projection-based model order reduction to accelerate analysis and optimization;and on a new concept of nonlinear active manifold (AM) to mitigate the curse of dimensionality during the training of projection-based reduced-order models (PROMs). The method of AM relies on the nonlinear concept of a deep convolutional autoencoder for dimensionality reduction: it is proposed as a superior alternative to the concept of an active subspace whose capabilities are limited by the capabilities of an affine approximation. The proposed computational framework also blends the concept of a global PROM as a surrogate model of a discretized nonlinear partial differential equation (PDE)based behavior function with that of a database of local, linear PROMs for approximating, wherever appropriate, a linear PDE-based behavior function. Adaptive parameter sampling is performed for training the global, nonlinear PROM as well as for populating the database of local, linear PROMs. The proposed computational framework is demonstrated for the solution of an MDAO problem formulated for a flexible instance of NASA's Common Research Model, where the objective function pertains to aerodynamics in the transonic regime, the constraint relates to flutter, and the design space contains 58 structural and aerodynamic shape design variables. The obtained numerical results illustrate the potential of the AM method for addressing the curse of dimensionality. They also demonstrate the feasibility of the computational framework for realistic MDA Oproblems and highlight its potential for reducing solution time.

关键词： Model Order Reduction Aerodynamic Configurations Computational Fluid Dynamics Fuselages Computational Structural Dynamics Greedy algorithm Structural Modeling Damping Ratio optimization algorithm Sweep Angle

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System Architecture optimization: An Open Source Multidisciplinary Aircraft Jet Engine Architecting Problem

System Architecture Optimization: An Open Source Multidiscip...

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AIAA Aviation Forum and Exposition

作者： Bussemaker, J. H. De Smedtt, T. La Rocca, G. Ciampa, P. D. Nagel, B. DLR German Aerosp Ctr Inst Syst Architectures Aeronaut MDO Grp Aircraft Design & Syst Integrat Hamburg Germany Delft Univ Technol Fac Aerosp Engn Flight Performance & Prop Delft Netherlands

ISBN: (数字)9781624106101

ISBN: (纸本)9781624106101

Decisions regarding the system architecture are important and taken early in the design process, however suffer from large design spaces and expert bias. Systematic design space exploration techniques, like optimization, can be applied to system architecting. Realistic engineering benchmark problems are needed to enable development of optimization algorithms that can successfully solve these black-box, hierarchical, mixed-discrete, multi-objective architecture optimization problems. Such benchmark problems support the development of more capable optimization algorithms, more suitable methods for modeling system architecture design space, and educating engineers and other stakeholders on system architecture optimization in general. In this paper, an engine architecting benchmark problem is presented that exhibits all this behavior and is based on the open-source simulation tools pyCycle and OpenMDAO. Next to thermodynamic cycle analysis, the proposed benchmark problem includes modules for the estimation of engine weight, length, diameter, noise and NOx emissions. The problem is defined using modular interfaces, allowing to tune the complexity of the problem, by varying the number of design variables, objectives and constraints. The benchmark problem is validated by comparing to pyCycle example cases and existing engine performance data, and demonstrated using both a simple and a realistic problem formulation, solved using the multiobjective NSGA-II algorithm. It is shown that realistic results can be obtained, even though the design space is subject to hidden constraints due to the engine evaluation not converging for all design points.

关键词： Aircrafts Afterburner Thermodynamic Cycles optimization algorithm Black Box Space Exploration Thrust Specific Fuel Consumption Turbofan Engines Airframes Model Based System Engineering

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Impact of Turbulence Models on Robust Aerodynamic Shape optimization of 3-D Wing Geometries

Impact of Turbulence Models on Robust Aerodynamic Shape Opti...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech Forum)

作者： Vuruskan, Aslihan Hosder, Serhat Florida Polytech Univ Mech Engn Dept Mech Engn Lakeland FL 33805 USA Missouri Univ Sci & Technol Aerosp Engn Dept Mech & Aerosp Engn Rolla MO 65409 USA

ISBN: (数字)9781624106095

ISBN: (纸本)9781624106095

The objective of this paper is to investigate the impact of two commonly used turbulence models in Reynolds-Averaged Navier-Stokes simulations (the Spalart-Allmaras and the Menter's Shear Stress Transport models) on the three-dimensional optimum wing design obtained with the gradient-based deterministic and robust aerodynamic shape optimization in transonic, viscous, turbulent flow. In particular, the main contribution of this study to aerodynamic design area is to evaluate the impact of turbulence models and different weight distributions in the multi-objective function (equal, mean-biased, and variance-biased) on the computational cost, optimal shape and its performance under Mach number uncertainty obtained with robust optimization. The results of the study show that the effect of the weight distribution in the objective function is more significant than the effect of turbulence model on the final shape obtained with robust design at lower off-design Mach numbers. Robust design tends to mitigate the impact of the turbulence model selection on the optimum shape and performance over the uncertain Mach number range, whereas the choice of the turbulence model becomes significant at off-design conditions for the optimal shapes obtained with deterministic design. This study also demonstrates the effectiveness of using stochastic expansions in robust aerodynamic shape optimization of three-dimensional wings.

关键词： Turbulence Models Aerodynamic Shape optimization Wing Geometry Robust Design Reynolds Averaged Navier Stokes Aerodynamic Flows Shear Stress Lift Coefficient Computational Fluid Dynamics optimization algorithm

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Building Geometry as a Variable in Energy, Comfort, and Environmental Design optimization-A Review from the Perspective of Architects

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BUILDINGS 2022年第1期12卷

作者： Kistelegdi, Istvan Horvath, Kristof Roland Storcz, Tamas Ercsey, Zsolt Univ Pecs Janos Szentagothai Res Ctr Fac Engn & Informat Technol Dept Bldg Struct & Energy DesignEnergia Design R H-7624 Pecs Hungary Univ Pecs Fac Engn & Informat Technol Dept Syst & Software Technol H-7624 Pecs Hungary

Due to negative environmental impacts caused by the building industry, sustainable buildings have recently become one of the most investigated fields in research. As the design technique itself is mainly responsible for building performance, building energy design optimization is of particular interest. Several studies concentrate on systems, operation, and control optimization, complemented by passive strategies, specifically related to the envelope. In building physics, different architectural considerations, in particular, the building's shape, are essential variables, as they greatly influence the performance of a building. Most scientific work that takes into consideration building geometry explores spaces without any energy optimization or calculates optimization processes of a few basic variables of simplified space geometries. Review studies mainly discuss the historic development of optimization algorithms, building domains, and the algorithm-system and software framework performance with coupling issues. By providing a systemized clustering of different levels of shape integration intensities, space creation principals, and algorithms, this review explores the current status of sustainability related shape optimization. The review proves that geometry design variable modifications and, specifically, shape generation techniques offer promising optimization potential;however, the findings also indicate that building shape optimization is still in its infancy.

关键词： building shape optimization building geometry design variables energy efficiency comfort performance building physics simulation optimization algorithm design method

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Multi-Working Point Performance optimization of Variable Cycle Engine Based on Ensemble Radial Basis Function Neural Network

Multi-Working Point Performance Optimization of Variable Cyc...

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AIAA Propulsion and Energy Forum / IEEE Electric Aircraft Technologies Symposium (EATS)

作者： Ye, Yifan Wang, Zhanxue Zhang, Xiaobo Northwestern Polytech Univ Sch Power & Energy Xian 710129 Shaanxi Peoples R China

ISBN: (数字)9781624106118

ISBN: (纸本)9781624106118

This paper proposed a multi-working point performance optimization algorithm based on the radial basis function neural network (RBFNN) for a variable cycle engine. The ensemble RBFNN is used to replace the engine performance simulation model during the optimization process to improve the optimization efficiency. Also, an enhanced multi-objective differential evolution algorithm is proposed to improve the Pareto solutions quality and convergence rate. Finally, a sample for three working points performance optimization of a double-bypass variable cycle engine is presented. This work optimizes a series of variable cycle engine design parameters and adjusting schedules, which consider high takeoff thrust and low cruise fuel consumption.

关键词： Neural Networks Variable Cycle Engines optimization algorithm Fuel Consumption Numerical Modeling High Pressure Turbine Bypass Ratio Particle Swarm optimization Air Flow Rate Compressor Surge

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Structural Design optimization Based on the Moving Baseline Strategy

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Acta Mechanica Solida Sinica 2020年第3期33卷 307-326页

作者： Xiaojun Wang Qiang Ren Wenpin Chen Yisi Liu Lei Wang Xuyun Ding Institute of Solid Mechanics School of Aeronautic Science and EngineeringBeihang UniversityBeijing 100083China Shanghai Marine Equipment Research Institute(SMERI) Shanghai 200031China

Structural design optimization has always been a topic of concern in industry because good design can improve the safety and economic efficiency of structures during their service *** the appropriate optimization algorithm is the key to solving structural optimal design *** this study,a new global optimization idea is proposed and named the moving baseline strategy.A baseline is initially set and will be repeatedly moving upward or downward to approach the optimal *** proposed strategy is a simple but effective,general,and stable algorithm that can be used to solve constrained and unconstrained structural optimization *** from traditional gradient-based,stochastic and heuristic algorithms,the developed algorithm provides a completely new idea to solve global or local optimization *** unconstrained and constrained numerical benchmark examples are used to test the proposed *** addition,structural optimal design problems of a ten-bar planar truss structure and a hypersonic wing structure(X-37B)are utilized to verify the effectiveness of the developed strategy in addressing structural design optimization problems in engineering.

关键词： Structural design Global optimization optimization algorithm Moving baseline strategy Constrained optimization

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An optimization iterative algorithm based on nonnegative constraint with application to Allan variance analysis technique

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ADVANCES IN SPACE RESEARCH 2014年第5期53卷 836-844页

作者： Lv, Hanfeng Zhang, Liang Wang, Dingjie Wu, Jie Natl Univ Def Technol Coll Aerosp Sci & Engn Staff Room Flight Dynam & Control Changsha 410073 Hunan Peoples R China

It is well known that inertial integrated navigation systems can provide accurate navigation information. In these systems, inertial sensor random error often becomes the limiting factor to get a better performance. So it is imperative to have accurate characterization of the random error. Allan variance analysis technique has a good performance in analyzing inertial sensor random error, and it is always used to characterize various types of the random error terms. This paper proposes a new method named optimization iterative algorithm based on nonnegative constraint applied to Allan variance analysis technique to estimate parameters of the random error terms. The parameter estimates by this method are nonnegative and optimal, and the estimation process does not have matrix nearly singular issues. Testing with simulation data and the experimental data of a fiber optical gyro, the parameters estimated by the presented method are compared against other excellent methods with good agreement;moreover, the objective function has the minimum value. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved.

关键词： Allan variance Parameter estimation optimization algorithm Inertial navigation Random error model

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