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AIAA SciTech Forum

作者： Longobardo, G. Catalano, P. Quagliarella, D. Tognaccini, R. Univ Napoli Federico II Ind Engn Dept Pzzle Tecchio 80 Naples Italy CIRA Italian Aerosp Res Ctr Fluid Mech Unit I-81043 Capua CE Italy

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper presents the application of a data-driven method to turbulence modelling. The main aim is enhancing the prediction capabilities of an existing RANS model for flows at low Reynolds number. The focus is placed on the laminar separation bubbles, the peculiar phenomenon of these flows. The recent-developed field inversion method has been chosen to infer corrections to the SST kappa -omega-gamma turbulence model. An innovative approach has been proposed to reduce the computational cost of the modelling procedure. The objective function makes use of the friction coefficient to compare data coming from the RANS model with reference to LES data available in literature. The performed test cases responded well to the optimization procedure which provided a correction field that boosts the turbulent kinetic energy within the bubble. The proposed procedure is the necessary building block to define a new turbulence model able of overcoming the present limitations in simulating flows at low Reynolds number.

关键词： Laminar Separation Bubble Large Eddy Simulation Reynolds Averaged Navier Stokes Spalart Allmaras Turbulence Model Low Reynolds Number Kinetic Energy Friction Coefficient Incompressible Flow optimization algorithm Velocity Profiles

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optimization of a Piezoelectric Wind-Excited Cantilever for Energy Harvesting from Facades 4th

Optimization of a Piezoelectric Wind-Excited Cantilever for ...

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4th International Conference of International-Federation-for-the-Promotion-of-Mechanism-and-Machine-Science ITALY (IFToMM ITALY)

作者： Tommasino, Domenico Moro, Federico Corona, Enrique de Pablo Vandi, Laura Baietta, Alessia Pracucci, Alessandro Doria, Alberto Univ Padua Padua Italy Smart Mat GmbH Dresden Germany Focchi Spa Poggio Torriana Italy

ISBN: (纸本)9783031107757;9783031107764

The optimization of the performance of a piezoelectric cantilever for energy harvesting from facades is concerned. The harvester is designed to exploit the vortex-induced vibrations due to the fluid-structure interaction between a cylindrical bluff body and the wind flow acting on the facade. An analytical lumped parameter model of the piezo-cantilever equipped with a cylindrical bluff body is provided to estimate the frequency response function between the aerodynamic tip force and the generated open circuit voltage. The analytical frequency response function is validated using experimental tests performed on a prototype of the piezo-cantilever. Finally, the design parameters of the harvester that maximize the generated voltage are determined using an optimization algorithm.

关键词： Energy harvesting Piezoelectric harvester Vortex shedding optimization algorithm Building facade

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Gravitational search algorithm : A novel approach for structural test path optimization

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JOURNAL OF INTERDISCIPLINARY MATHEMATICS 2020年第2期23卷 471-480页

作者： Rathee, Nisha Chhillar, Rajender Singh Indira Gandhi Delhi Tech Univ Women Dept Informat Technol Delhi 110006 India Maharshi Dayanand Univ Dept Comp Sci & Applicat Rohtak 124001 Haryana India

Structural testing examines the internal structure or code of the program in which knowledge to the internal structure is must to generate effective number of test cases for identifying logical and semantic errors in the program. Basis path testing is a structural based testing technique whose main objective is to test each independent test path in the program at least once. It is very difficult for the tester of the software to test each generated test path therefore, optimization of test path is required to save time, cost and effort. This paper proposes a new meta-heuristic technique i.e Gravitational Search algorithm (GSA) for optimization of test paths. The results obtained from newly generated meta-heuristic algorithm are compared with Simple Genetic algorithm (SGA) and the results shows that GSA gives better results as compared to SGA.

关键词： Path Testing optimization algorithm Gravitational Search Alg

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Range Extension using Reinforcement Learning for High Altitude Aircraft

Range Extension using Reinforcement Learning for High Altitu...

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AIAA SciTech Forum

作者： Allik, Bethany Maley, James Shedleski, Franklin US Army Combat Capabil Dev Command Army Res Lab FCDD RLW WE Aberdeen Proving Ground MD 21005 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

Recently, enabling long duration autonomy for aerial systems has been of significant interest amonst researchers and industry experts. These systems can use atmospheric effects for improved lift, reduced drag at high altitudes and optimal glide slopes to their advantage to improve flight time and ranges. Much work has been done algorithmically by posing optimization problems for maximizing power, and improving lift and glide of flight vehicles using best known practices of aerospace engineering. New techniques include deep learning and specifically reinforcement learning. These data-driven approaches have shown promise as low-level flight controllers and path planners. Here we present a method using reinforcement learning to extend range of a flight vehicle. The method we use is the popular Proximal Policy optimization algorithm for learning. The final policy is an end-to-end, low-level controller along with path planner aimed to optimize distance traveled for high flying vehicles.

关键词： Reinforcement Learning High Altitude Aircraft Lift to Drag Ratio Neural Networks Level Flight optimization algorithm Aerospace Engineering Angle of Attack Drag Coefficient Control optimization

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Design optimisation of separate-jet exhausts with CFD in-the-loop and dimensionality reduction techniques

Design optimisation of separate-jet exhausts with CFD in-the...

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AIAA SciTech Forum

作者： Hueso-Rebassa, J. MacManus, D. Tejero, F. Goulos, I. Abdessemed, C. Sheaf, C. Cranfield Univ Prop Engn Ctr Cranfield MK43 0AL Beds England Rolls Royce plc Derby DE24 8BJ England

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

For Ultra-High Bypass Ratio aero-engines, the exhaust system is likely to play a significant role on the aerodynamics and performance of the aircraft. For this reason, relatively rapid methods for the aerodynamic design and optimisation of exhaust systems are required to inform design decisions at early stages of the design process. Previous exhaust optimisation works encompassed Response Surface Model (RSM) based optimisations of nozzle configurations that were parametrised with a significant number of design variables. The RSM were constructed with a large database of designs that were assessed with fine computational meshes and well resolved boundary layers. However, the large number of design variables and the computational cost required to evaluate each exhaust design limited the optimisation capabilities. This work develops a relatively more rapid exhaust optimisation method based on CFD in-the-loop and dimensionality reduction. The methodology is based on coarse meshes and wall functions to guide the optimisation process and is coupled with methods for the identification of the dominant design variables. For an UHBR aero-engine exhaust design space of 16 design variables, it was found that the velocity coefficient could be characterised with only seven parameters. Based on these results, various optimisation methods were developed and applied. These targeted the maximisation of the velocity coefficient by optimising just the 7 dominant design variables. With these approaches, a similar benefit in exhaust performance relative to the baseline optimisation method was obtained approximately 4 times faster.

关键词： Computational Fluid Dynamics Exhaust Velocity Design optimization Overall Pressure Ratio optimization algorithm Boundary Layers Aerodynamic Design optimization Surface Modeling Database Design Flow Conditions

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A Review of the Optimal Configuration of the "PV-Energy Storage-Direct Current-Flex" System 6

A Review of the Optimal Configuration of the "PV-Energy Stor...

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6th Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Liu, Siqi Cai, Dongsheng Lu, Huanli Ukwuoma, Chiagoziem Chima Luo, Shuang Yang, Zhihui Chengdu Univ Technol Sch Nucl Technol & Automat Engn Chengdu Peoples R China Guiyang Engn Corp Ltd Guiyang Peoples R China

ISBN: (纸本)9798350373486;9798350373479

In the context of large-scale new energy applications and "carbon peak" and "carbon neutrality", Photovoltaic power generation because of its clean and easy large-capacity storage characteristics. It can improve the utilization rate of new energy and the stability of power system operation, and further reduce carbon emissions, so photovoltaic power generation has become one of the future energy sources that are vigorously developed. The capacity configuration model and solution method are the focus of the current research. This paper summarizes the research status of optimal scheduling of integrated energy systems from two aspects: scheduling model and method. According to the difference of research priorities, the basic optimal scheduling model and the optimal scheduling model considering the randomness of the system are discussed respectively. The solution methods are divided into analytical method and artificial intelligence method. Finally, a vision for the future is put forward based on the existing problems.

关键词： Capacity configuration optical storage direct and flexible optimization algorithm

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Impact of Disparate Goals on Evader Performance in an Orbital Pursuer-Evader Game

Impact of Disparate Goals on Evader Performance in an Orbita...

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AIAA SciTech Forum

作者： Halsey, Stephanie L. Curtis, David H. Prince, Eric R. Air Force Inst Technol Dept Aeronaut & Astronaut 2950 Hobson Way Wright Patterson AFB OH 45433 USA Space Warfighting Anal Ctr Wargame Modeling Div Colorado Springs CO 80916 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This report investigates trends in an orbital pursuer-evader game, particularly when the evader follows a suboptimal path misaligned with the game's primary objective. To compute the pursuer's trajectories in response to the evader's chosen path, two distinct methods are employed: the Indirect Heuristic Method (IHM) and the Linear Quadratic Regulator (LQR). While IHM assumes full knowledge of the evader's trajectory, LQR operates with only the evader's present state information. When employing the IHM reoptimization approach for the pursuer, the results reveal that the rendezvous path is the least advantageous for the evader. Conversely, when utilizing the LQR controller, which aligns more closely with real-world scenarios, results indicate that the rendezvous path is actually the most advantageous while the intercept path is least favorable. Overall, this work provides insight into open-loop evader strategies and serves as a baseline to compare other strategies in orbital conflict amidst uncertainty.

关键词： Linear Quadratic Regulator Optimal Control Problem Particle Swarm optimization Tschauner Hempel Equations Satellites optimization algorithm Orbital Dynamics Thrust Vectoring Periapsis Genetic algorithm

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An improved optimization framework for evacuation planning in facilities considering pedestrian dynamics

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JOURNAL OF TRANSPORTATION SAFETY & SECURITY 2022年第4期14卷 693-722页

作者： Tang, Tie-Qiao Zhang, Bo-Tao Wang, Tao Beihang Univ Sch Transportat Sci & Engn Beijing Key Lab Cooperat Vehicle Infrastruct Syst Beijing 100191 Peoples R China

To take advantage of enormous evacuation data for the efficiency of emergency evacuation, we in this paper develop an improved framework for evacuation planning, which is composed of a modified network-based model and a corresponding optimization structure. Under the proposed framework, we describe pedestrians' features in a computationally efficient way according to their movement pattern and design a model where a typical optimization problem can be well integrated. At the optimization stage, we present a novel algorithm that is more suitable for the allocation of network nodes. For validation, the framework is applied based on the data from experiments and a specific scenario to simulate emergency evacuation. The preliminary numerical results show that a satisfactory evacuation plan can be developed within an acceptable calculation time. Combining with other supporting data, the framework can be further extended to investigate other evacuation strategies in large-scale and crowded pedestrian facilities.

关键词： evacuation planning network-based model optimization algorithm pedestrian

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Reinforcement Learning for the Interception of Hypersonic Vehicles

Reinforcement Learning for the Interception of Hypersonic Ve...

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AIAA SciTech Forum

作者： Porter, Daniel M. Gilbert, John Virginia Polytech Inst & State Univ Aerosp Engn Blacksburg VA 24060 USA Virginia Polytech Inst & State Univ Mission Syst Div Blacksburg VA 24060 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

This paper explores using reinforcement learning for the guidance of an interceptor protecting a stationary target against an attacking hypersonic vehicle. The performance of the reinforcement learning-trained algorithm is compared to a traditional guidance method for interception. Proximal policy optimization (PPO) is the algorithm selected to guide the interceptor, and proportional navigation is the chosen traditional method that has been historically implemented. Both methods provide acceleration commands to the interceptor, while the attacker maintains a constant velocity and linear trajectory towards the target. PPO uses an actor-critic framework and samples the environment for an action based on the current policy. Although further work is required for the PPO algorithm to improve upon the performance of traditional methods, it may provide more effective and efficient interception against a highly maneuverable attacking vehicle.

关键词： Hypersonic Vehicles Reinforcement Learning Proportional Navigation Interceptor Guidance Neural Networks Flight Path Angle Surface to Air Missile optimization algorithm Gradient Method Value Function

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Reduced-Order Representation and Effective Property Prediction of CNT-Polymer Nanocomposites using Convolutional Neural Networks and Autoencoders

Reduced-Order Representation and Effective Property Predicti...

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AIAA SciTech Forum

作者： Shah, Kavan Seidel, Gary D. Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA Virginia Tech Kevin T Crofton Dept Aerosp & Ocean Engn Blacksburg VA 24061 USA

ISBN: (数字)9781624107115

ISBN: (纸本)9781624107115

In this work, Convolutional Neural Networks (CNNs) are trained to predict the effective stiffness, electrical conductivity, and piezoresistivity of computationally generated CNT-polymer composite statistical volume elements (SVEs). It is found that the distributions of the properties predicted by the CNN accurately match those predicted by FEM. However, the CNN fails to predict the properties of individual SVEs with sufficient accuracy. Further, multi-objective supervised autoencoders (SAEs) are trained to obtain a reduced-order representation that allows the prediction of the effective stiffness of the SVEs while simultaneously reconstructing the SVEs accurately. The SAE predicts the distribution of the stiffness at all CNT volume fractions with adequate accuracy, but fails to predict the stiffness of individual SVEs with sufficient accuracy.

关键词： Convolutional Neural Network Carbon Nanotubes Electrical Conductivity Polymer Nanocomposites Finite Element Analysis Reduced Order Modelling Microstructure optimization algorithm Electrical Impedance Space Imaging

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