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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： You, Hojun Kim, Chongam Seoul Natl Univ Dept Mech & Aerosp Engn Seoul 08826 South Korea Seoul Natl Univ Dept Mech & Aerosp Engn Inst Adv Aerosp Technol Seoul 08826 South Korea

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

A sophisticated solution algorithm with complex data structures is a crucial factor hindering both academia and industry from utilizing high-order methods. At the same time, compact data structures may also provide a wide variety of chances for improving the performance of the solution algorithm via optimization. In order to address this issue, we present an architecture-based and target-orienteda* optimization algorithm for high-order methods based on complete-search tensor contractions (CsTC). After briefly explaining the general framework of the proposed optimization procedure, CsTC is applied to the discontinuous Galerkin (DG) method. Among various ways to realize tensor contractions (TC), the GEMM (GEneral Matrix Multiplication)-based approach is adopted owing to its flexibility to handle moderate order of TC and the reusability of state-of-the-art GEMM primitives. One distinctive feature of the current optimization procedure is that both an optimized way to compute tensors and optimized index order of the associated tensors are simultaneously considered by not fixing the index order in a priori. The optimization procedure is then converted into an optimization problem to minimize the computational cost, and the optimized TC is inductively found by searching all candidates in search space. The whole process can be done automatically in a few minutes on a computer. The performance enhancement is determined by the mesh and solution parameters to be employed as well as a processor to be used, i.e. the CsTC optimization procedure is completely target-oriented and architecture-based. Various parameters and processors with different micro-architectures are investigated and compared with one another. All results verify that greater performance enhancement can be obtained as the order of the DG approximation increases and a more recent processor is employed. The CsTCa* optimization algorithm applied to a 3-D sinewave viscous flow governed by the compressible Navier-Stokes

关键词： Solution algorithm Navier Stokes Equations optimization algorithm Unsteady Flows Discontinuous Galerkin Method Tetrahedral Element Aspect Ratio Finite Volume Method Artificial Neural Network Numerical Simulation

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Full 3D Model for Asteroids Dynamics Estimation in a Monocular Vision-Based System

Full 3D Model for Asteroids Dynamics Estimation in a Monocul...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Gunawan, Charlie Bachtiar Magnani, Guido Monge-Raluy, Neus Gortazar-Martinez, Isabel Zenou, Emmanuel Univ Toulouse ISAE SUPAERO Sci F-31400 Toulouse France Univ Toulouse ISAE SUPAERO F-31400 Toulouse France Univ Toulouse ISAE SUPAERO Dept Complex Syst Engn F-31400 Toulouse France

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

In early-phase deep space exploration missions, an on-board monocular vision-based navigation system capable of characterizing an asteroid's shape and dynamics allows an increase in both autonomy and redundancy resulting in more efficient operations in terms of time and cost. This paper introduces a solely monocular, vision-based method that can robustly estimate the full 3D dynamics of an asteroid. The approach takes a sequence of 2D trajectories as input and estimates the parameters of the asteroid motion using a global optimization technique. This study compares two methods in terms of robustness and accuracy. Advancing from previous works, a reconstruction of a full 3D model is shown to be possible benefiting only from one monocular vision-based system.

关键词： Asteroids Vision Based Navigation Deep Space Exploration Space Missions optimization algorithm Light Detection and Ranging Simultaneous Localization and Mapping Additive White Gaussian Noise Autonomous Systems Search algorithm

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A Modified ABC-SQP-Based Combined Approach for the optimization of a Parallel Hybrid Electric Vehicle

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ENERGIES 2020年第17期13卷 4529页

作者： Shivappriya, S. N. Karthikeyan, S. Prabu, S. Perez de Prado, R. Parameshachari, B. D. Kumaraguru Coll Technol Coimbatore 641049 Tamil Nadu India M Kumarasamy Coll Engn Karur 639113 Tamil Nadu India Mahendra Inst Technol Namakkal 637503 Tamil Nadu India Univ Jaen Telecommun Engn Dept Jaen 23700 Spain GSSS Inst Engn & Technol Women Mysuru 570016 India

In this paper, an improved fuel consumption and emissions control strategy based on a mathematical and heuristic approach is presented to optimize Parallel Hybrid Electric Vehicles (HEVs). The well-known Sequential Quadratic Programming mathematical method (SQP-Hessian approach) presents some limitations to achieve fuel consumption and emissions control optimization, as it is not able to find the global minimum, and it generally shows efficient results in local exploitation searches. The usage of a combined Modified Artificial Bee Colony algorithm (MABC) with the SQP approach is proposed in this work to obtain better optimal solutions and overcome these limitations. The optimization is performed with boundary conditions, considering that the optimized vehicle performance has to satisfy Partnership for a New Generation of Vehicles (PNGV) constraints. The weighting factor of the vehicle's performance parameters in the objective function is varied, and optimization is carried out for two different driving cycles, namely Federal Test Procedure (FTP) and Economic commission Europe-Extra Urban Driving Cycle (ECE-EUDC), using the MABC and MABC with SQP approaches. The MABC with SQP approach shows better performance in terms of fuel consumption and emissions than the pure heuristic approach for the considered vehicle with similar boundary conditions. Moreover, it does not present significant penalties for final battery charging and it offers an optimized size of the key vehicle's components for different driving cycles.

关键词： automotive system SQP approach dynamic optimization parallel hybrid electric vehicle artificial bee colony optimization algorithm

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Displacement Controlled 2D Compliant Mechanisms for use in Morphing Structures

Displacement Controlled 2D Compliant Mechanisms for use in M...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Munroe, Evan Bohrer, Rubens Chishty, Wajid Ali Kim, Il Yong Queens Univ Dept Mech & Mat Engn Kingston ON K7L 2N8 Canada Natl Res Council Canada NRC Aerosp Res Ctr Ottawa ON K1A 0R6 Canada

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Morphing materials and structures capable of assuming specific shapes under precise conditions have become increasingly relevant for numerous industries, including aerospace. However, a significant limitation of many proposed morphing materials and structures is their manufacturability. Furthermore, many morphing materials are not capable of supporting significant loads independently. It is therefore of interest to develop a methodology to generate support structures for morphing materials that can support specific desired shapes, and can be manufactured easily. This work proposes a methodology for optimizing compliant mechanisms that can be 3D printed using commercially available materials. These mechanisms are designed to lie behind traditional morphing materials such as shape-memory alloys, and assume a specific displacement profile when subjected to certain loading conditions. The methodology is tested on two separate displacement profiles and is compared with respect to buckling constraint severity.

关键词： Shape Memory Alloys Topology optimization Smart Materials optimization algorithm Poisson's Ratio Thermoplastic Polyurethane Aircraft Structures Stiffness Matrices Computational Fluid Dynamics Simulation Material Properties

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Increasing the essential oil yield from Shorea roxburghii inflorescences using an eco-friendly solvent-free microwave extraction method for fragrance applications

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JOURNAL OF APPLIED RESEARCH ON MEDICINAL AND AROMATIC PLANTS 2021年 24卷

作者： Yingngam, Bancha Navabhatra, Abhiruj Brantner, Adelheid Ubon Ratchathani Univ Fac Pharmaceut Sci Dept Pharmaceut Chem & Technol Ubon Ratchathani 34190 Thailand Rangsit Univ Coll Pharm Dept Pharmacol Pathum Thani 12000 Thailand Karl Franzens Univ Graz Inst Pharmaceut Sci Dept Pharmacognosy Univ Pl 4-1 A-8010 Graz Austria

The short bloom period of Shorea roxburghii *** is a technical challenge in the development of a fast and efficient process for recovering essential oils. This study aimed to optimize the conditions for the isolation of essential oil and the determination of the resulting oil's possible bioactivities for fragrance applications. Solvent-free microwave extraction (SFME) coupled with response surface methodology (RSM) was applied to study essential oil from fresh inflorescences. The microwave power and irradiation time were chosen as crucial independent variables, while essential oil yield was the response variable. The physicochemical, antioxidant, olfactory, cytotoxic, and irritant properties of the resulting oil were evaluated. SFME yielded essential oil with a unique and pleasant scent, similar to that of fresh flowers. Increased microwave power and irradiation time increased the oil yield. The proportions of oxygenated compounds with high antioxidant properties were notably higher after SFME than after hydrodistillation. The optimal conditions for maximizing the oil yield were 800 W and 35 min. Among 51 volatile substances, germacrene B, kaur-16-ene, beta-caryophyllene, and (E)-linalool oxide (furanoid) were the oil's main components. Low toxicity to primary human dermal fibroblast cells was defined at an oil concentration < 50 mu g/mL. Moreover, the application of the extracted volatile oil to hen's egg-chorioallantoic membrane resulted in a low degree of irritation. This research establishes optimal extraction conditions for the isolation of essential oil with potential for fragrance applications.

关键词： Aromatic plants Essential oil Extraction conditions Fragrance ingredients optimization algorithm Response surface methodology

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Collocation method and model predictive control for optimal control strategies of edl vehicles

Collocation method and model predictive control for optimal ...

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Accelerating Space Commerce, Exploration, and New Discovery Conference, ASCEND 2020

作者： Srinivas, Neeraj Schroeder, Kevin K. Kevin T. Crofton Department of Aerospace and Ocean Engineering Virginia Tech Virginia Polytechnic Institute and State University BlacksburgVA20460 United States Hume Center for National Security and Technology Virginia Tech Virginia Polytechnic Institute and State University BlacksburgVA20460 United States

ISBN: (数字)9781624106088

ISBN: (纸本)9781624106088

Traditional planetary exploration methods consist of an entry-descent-landing (EDL) vehicle which targets a pre-determined location on the planet to execute a high-precision landing. Although this process has a good scientific return, it is often extremely expensive, requiring long development and limited flexibility in terms of unexpected scenarios. This work aims to lay the foundations for a fully autonomous EDL vehicle which autonomously land at various targets with high scientific output potential, while taking the necessary control strategies for course correction. The vehicle calculates its’ optimal control strategy for landing at the chosen target location through the collocation method, and the model predictive control (MPC) strategy is used to enforce the control strategies. © 2020 The MITRE Corporation. All Rights Reserved.

关键词： Collocation Method Optimal Control Model Predictive Control Planets Planetary Exploration Bank Angle Flight Path Angle Heading Angle optimization algorithm Angle of Attack

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An optimization Tool for Preliminary Concept Design of Guided Air to Ground Munitions

An Optimization Tool for Preliminary Concept Design of Guide...

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AIAA Aviation Forum and Exposition

作者： Varol, Deniz Akgul, Mehmet Aydin, Gune Roketsan Missile Ind Inc Tact Munit Syst Div TR-06780 Ankara Turkiye

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

Preliminary concept design is the starting step of constructing a new guided munition and needs a multidisciplinary approach. Efficiency and robustness of a concept design procedure directly affect the detailed design process. Preliminary concept designs mostly fail or being changed due to aerodynamical analysis or flight simulation results. Failures or changes in preliminary concept designs are time consuming and result in loss of workforce. Finding a design tool, which initially covers aerodynamical properties of each possible design and optimize the design with respect to the simulation results, is a necessity. This paper presents a tool for finding the optimal preliminary concept design solution in a design space of possible guided munitions with the use of Design of Experiments (DoE) technique, genetic algorithm and 3 DoF flight simulation.

关键词： Genetic algorithm Flight Simulation Design of Experiments Aerodynamic Configurations Regression Analysis Autopilot State Space Representation Yaw Biased Proportional Navigation Guidance optimization algorithm

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Finding Maximum Expected Improvement for High-Dimensional Design optimization

Finding Maximum Expected Improvement for High-Dimensional De...

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AIAA Aviation Forum and Exposition

作者： Zhang, Yiming Kristensen, Jesper Ghosh, Sayan Vandeputte, Thomas Tallman, James Khan, Genghis Wang, Liping GE Res One Res Circle K1-4B16 Niskayuna NY 12309 USA GE Res One Res Circle K1-4B18B Niskayuna NY 12309 USA GE Res One Res Circle K1-4B20 Niskayuna NY 12309 USA GE Res One Res Circle K1-2C24A Niskayuna NY 12309 USA GE Res One Res Circle ES 103 Niskayuna NY 12309 USA GE Res One Res Circle K1 4B8A Niskayuna NY 12309 USA GE Res One Res Circle K1-4B3A Niskayuna NY 12309 USA

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

Industrial design fundamentally relies on high-dimensional multi-objective optimization. Bayesian Global optimization (BGO) based on Gaussian Processes (GPs) has been a major tool for this practice where new designs are picked in each iteration of the optimization with maximum Expected Improvement (EI). Rich effort has been made to improve the accuracy of GPs for high-dimensional space approximation. However, finding the maximum-EI points from a developed surrogate can be challenging in a high-dimensional space and has a significant influence on the optimization process. Finding maximum-EI points with a GP is an optimization process over 1 output dimension (the EI) and multiple input dimensions (the design space) where the optimum is the next candidate point which is predicted to mostly improve the current best optima. This paper explores the EI optimization for high-dimensional design optimization from two algebraic functions and one industrial application. Eleven optimization technologies are compared towards EI-maximization and the effect of the sample size is also discussed. Significant variations are observed between the algorithms for finding the maximum EI. Based on a heuristic comparison, Particle Swarm optimization and Covariance Matrix Adaptation Evolution Strategy outperformed other optimization methods. The paper then investigates the influence of EI optimization on the performance of the overall BGO optimization task. It is found that picking new sample at the point with maximum EI does not necessarily provide the best BGO performance. The values of EI are only mildly correlated with the actual optima in each iteration across the design space. This could suggest that alternative metrics and/or methods for optimization have the potential to perform much better than EI.

关键词： Particle Swarm optimization Cumulative Distribution Function Industrial Applications Surrogate Model Genetic algorithm optimization algorithm Design of Experiments Kernel Functions Rotor Blades Annealing

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On the use of upper trust bounds in constrained Bayesian optimization infill criterion

On the use of upper trust bounds in constrained Bayesian opt...

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AIAA Aviation Forum and Exposition

作者： Priem, R. Bartoli, N. Diouane, Y. Univ Toulouse DTIS ONERA Informat Proc & Syst Dept Toulouse France Univ Toulouse ISAE SUPAERO Complexes Syst Engn Dept F-31055 Toulouse 4 France

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

In order to handle constrained optimization problems with a large number of design variables, a new approach has been proposed to address constraints in a surrogate-based optimization framework. This approach focuses on sequential enrichment using adaptive surrogate models based on Bayesian optimization approach, and Gaussian process models. A constraints criterion using the uncertainty estimation of the Gaussian process models is introduced. Different evolutions of the algorithm, based on the accuracy of the constraints surrogate models, are used for selecting the infill sample points. The resulting algorithm has been tested on the well known modified Branin optimization problem.

关键词： Bayesian optimization Surrogate Model Probability Distribution Functions Aircraft Design Process Hybrid Electric Vehicle Design of Experiments Aeroplane optimization algorithm Aircraft Configurations Neural Networks

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Effect of Turbulence Models on Aerodynamic optimization of 3-D Wing Geometries

Effect of Turbulence Models on Aerodynamic Optimization of 3...

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AIAA Aviation Forum and Exposition

作者： Vuruskan, Aslihan Hosdert, Serhat Missouri Univ Sci & Technol Dept Aerosp & Mech Engn Rolla MO 65409 USA Missouri Univ Sci & Technol Dept Aerosp & Mech Engn Aerosp Engn Rolla MO 65409 USA

ISBN: (数字)9781624105890

ISBN: (纸本)9781624105890

The objective of this paper is to investigate the impact of two commonly used turbulence models in Reynolds Averaged Navier-Stokes simulations on three-dimensional optimum design obtained with the gradient-based deterministic aerodynamic shape optimization in transonic, viscous, turbulent flow. The main contribution of this study to aerodynamic design area is to evaluate the impact of turbulence models on the computational cost, optimal shape and its performance obtained with deterministic optimization. The two turbulence models investigated include Spalart-Allmaras and the Menter's Shear Stress Transport models. In this study, Free-Form Deformation is utilized as shape parameterization technique. The results of the current study show that deterministic design tends to reduce the impact of the turbulence model selection on the optimum shape and performance at deterministic Mach number. It is also observed that the computational cost of optimization is almost constant for each turbulence model.

关键词： Turbulence Models Wing Geometry Aerodynamic Shape optimization Reynolds Averaged Navier Stokes Aerodynamic Flows Shear Stress Aerodynamic Performance Computational Fluid Dynamics optimization algorithm Finite Volume Method

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