Robust design and optimization of UAV empennage

2017 ◽  
Vol 89 (4) ◽  
pp. 609-619 ◽  
Author(s):  
Witold Artur Klimczyk ◽  
Zdobyslaw Jan Goraj
Keyword(s):  
Optimization Problems ◽  
Computational Cost ◽  
Aircraft Design ◽  
Computational Time ◽  
Wing Design ◽  
Surrogate Modelling ◽  
Content Type ◽  
Design And Optimization ◽  
Unmanned Air Vehicle ◽  
Splitting Problem

Purpose This paper aims to address the issue of designing aerodynamically robust empennage. Aircraft design optimization often narrowed to analysis of cruise conditions does not take into account other flight phases (manoeuvres). These, especially in unmanned air vehicle sector, can be significant part of the whole flight. Empennage is a part of the aircraft, with crucial function for manoeuvres. It is important to consider robustness for highest performance. Design/methodology/approach Methodology for robust wing design is presented. Surrogate modelling using kriging is used to reduce the optimization cost for high-fidelity aerodynamic calculations. Analysis of varying flight conditions, angle of attack, is made to assess robustness of design for particular mission. Two cases are compared: global optimization of 11 parameters and optimization divided into two consecutive sub-optimizations. Findings Surrogate modelling proves its usefulness for cutting computational time. Optimum design found by splitting problem into sub-optimizations finds better design at lower computational cost. Practical implications It is demonstrated, how surrogate modelling can be used for analysis of robustness, and why it is important to consider it. Intuitive split of wing design into airfoil and planform sub-optimizations brings promising savings in the optimization cost. Originality/value Methodology presented in this paper can be used in various optimization problems, especially those involving expensive computations and requiring top quality design.

Aerodynamic design and optimization of propellers for multirotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Witold Artur Klimczyk
Keyword(s):  
Structural Integrity ◽  
Navier Stokes ◽  
Optimization Approach ◽  
Minimum Thickness ◽  
Design Problems ◽  
Content Type ◽  
Design And Optimization ◽  
Propeller Design ◽  
Unmanned Air Vehicle ◽  
Propeller Blades

Purpose This paper aims to present a methodology of designing a custom propeller for specified needs. The example of propeller design for large unmanned air vehicle (UAV) is considered. Design/methodology/approach Starting from low fidelity Blade Element (BE) methods, the design is obtained using evolutionary algorithm-driven process. Realistic constraints are used, including minimum thickness required for stiffness, as well as manufacturing ones – including leading and trailing edge limits. Hence, the interactions between propellers in hex-rotor configuration, and their influence on structural integrity of the UAV are investigated. Unsteady Reynolds-Averaged Navier–Stokes (URANS) are used to obtain loading on the propeller blades in hover. Optimization of the propeller by designing a problem-specific airfoil using surrogate modeling-driven optimization process is performed. Findings The methodology described in the current paper proved to deliver an efficient blade. The optimization approach allowed to further improve the blade efficiency, with power consumption at hover reduced by around 7%. Practical implications The methodology can be generalized to any blade design problem. Depending on the requirements and constraints the result will be different. Originality/value Current work deals with the relatively new class of design problems, where very specific requirements are put on the propellers. Depending on these requirements, the optimum blade geometry may vary significantly.

Convolutional Neural Networks for the Localization of Plastic Velocity Gradient Tensor in Polycrystalline Microstructures

2021 ◽  
pp. 1-41
Author(s):  
David Montes de Oca Zapiain ◽  
Apaar Shanker ◽  
Surya Kalidindi
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Velocity Gradient ◽  
Optimization Problems ◽  
Computational Cost ◽  
Two Phase ◽  
Design And Optimization ◽  
Gradient Fields ◽  
Generalized Spherical Harmonics ◽  
New Research

Abstract Recent work has demonstrated the potential of convolutional neural networks (CNNs) in producing low-computational cost surrogate models for the localization of mechanical fields in two-phase microstructures. The extension of the same CNNs to polycrystalline microstructures is hindered by the lack of an efficient formalism for the representation of the crystal lattice orientation in the input channels of the CNNs. In this paper, we demonstrate the benefits of using generalized spherical harmonics (GSH) for addressing this challenge. A CNN model was successfully trained to predict the local plastic velocity gradient fields in polycrystalline microstructures subjected to a macroscopically imposed loading condition. Specifically, it is demonstrated that the proposed approach improves significantly the accuracy of the CNN models, when compared with the direct use of Bunge-Euler angles to represent the crystal orientations in the input channels. Since the proposed approach implicitly satisfies the expected crystal symmetries in the specification of the input microstructure to the CNN, it opens new research directions for the adoption of CNNs in addressing a broad range of polycrystalline microstructure design and optimization problems.

Simulating swing dynamics of a power system model using nonlinear model order reduction

2019 ◽  
Vol 38 (6) ◽  
pp. 1918-1930
Author(s):  
Satyavir Singh ◽  
Mohammad Abid Bazaz ◽  
Shahkar Ahmad Nahvi
Keyword(s):  
Power System ◽  
System Dynamics ◽  
Interpolation Method ◽  
Computational Cost ◽  
Nonlinear Function ◽  
Computational Time ◽  
Reduced Basis ◽  
Power System Dynamics ◽  
Content Type ◽  
On Line

Purpose The purpose of this paper is to demonstrate the applicability of the Discrete Empirical Interpolation method (DEIM) for simulating the swing dynamics of benchmark power system problems. The authors demonstrate that considerable savings in computational time and resources are obtained using this methodology. Another purpose is to apply a recently developed modified DEIM strategy with a reduced on-line computational burden on this problem. Design/methodology/approach On-line computational cost of the power system dynamics problem is reduced by using DEIM, which reduces the complexity of the evaluation of the nonlinear function in the reduced model to a cost proportional to the number of reduced modes. The on-line computational cost is reduced by using an approximate snap-shot ensemble to construct the reduced basis. Findings Considerable savings in computational resources and time are obtained when DEIM is used for simulating swing dynamics. The on-line cost implications of DEIM are also reduced considerably by using approximate snapshots to construct the reduced basis. Originality/value Applicability of DEIM (with and without approximate ensemble) to a large-scale power system dynamics problem is demonstrated for the first time.

A parallel multi-fidelity optimization approach in induction hardening

2019 ◽  
Vol 39 (1) ◽  
pp. 133-143
Author(s):  
Marco Baldan ◽  
Alexander Nikanorov ◽  
Bernard Nacke
Keyword(s):  
Parallel Computing ◽  
Optimization Technique ◽  
Computational Cost ◽  
Induction Hardening ◽  
Computational Time ◽  
High Fidelity ◽  
Optimization Approach ◽  
Content Type ◽  
Fidelity Model ◽  
Hardening System

Purpose Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.

Practical problems of numerical optimization in aerospace sciences

2017 ◽  
Vol 89 (4) ◽  
pp. 570-578 ◽  
Author(s):  
Jacek Mieloszyk
Keyword(s):  
Design Process ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
Aircraft Design ◽  
Lessons Learned ◽  
Multidisciplinary Optimization ◽  
Content Type ◽  
Design Procedures ◽  
Industry Practice

Purpose The paper aims to apply numerical optimization to the aircraft design procedures applied in the airspace industry. Design/methodology/approach It is harder than ever to achieve competitive construction. This is why numerical optimization is becoming a standard tool during the design process. Although optimization procedures are becoming more mature, yet in the industry practice, fairly simple examples of optimization are present. The more complicated is the task to solve, the harder it is to implement automated optimization procedures. This paper presents practical examples of optimization in aerospace sciences. The methodology is discussed in the article in great detail. Findings Encountered problems related to the numerical optimization are presented. Different approaches to the solutions of the problems are shown, which have impact on the time of optimization computations and quality of the obtained optimum. Achieved results are discussed in detail with relation to the used settings. Practical implications Investigated different aspects of handling optimization problems, improving quality of the obtained optimum or speeding-up optimization by parallel computations can be directly applied in the industry optimization practice. Lessons learned from multidisciplinary optimization can bring industry products to higher level of performance and quality, i.e. more advanced, competitive and efficient aircraft design procedures, which could be applied in the industry practice. This can lead to the new approach of aircraft design process. Originality/value Introduction of numerical optimization methods in aircraft design process. Showing how to solve numerical optimization problems related to advanced cases of conceptual and preliminary aircraft design.

Design strategies for multi-objective optimization of aerodynamic surfaces

2017 ◽  
Vol 34 (5) ◽  
pp. 1724-1753 ◽  
Author(s):  
Anand Amrit ◽  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Pareto Front ◽  
Design Space ◽  
Optimization Problems ◽  
Computational Time ◽  
High Fidelity ◽  
Design Strategies ◽  
Aerodynamic Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Aerodynamic Surfaces

Purpose This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level. Design/methodology/approach Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate. Findings Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day. Originality/value The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

Fast sparse image reconstruction method in through-the-wall radars using limited memory Broyden–Fletcher–Goldfarb–Shanno algorithm

2021 ◽  
pp. 1-11
Author(s):  
Candida Mwisomba ◽  
Abdi T. Abdalla ◽  
Idrissa Amour ◽  
Florian Mkemwa ◽  
Baraka Maiseli
Keyword(s):  
Image Reconstruction ◽  
Large Scale ◽  
Optimization Problems ◽  
Classical Method ◽  
Computational Cost ◽  
Least Square ◽  
Computational Time ◽  
Reconstruction Method ◽  
Nonlinear Methods ◽  
Limited Memory

Abstract Compressed sensing allows recovery of image signals using a portion of data – a technique that has drastically revolutionized the field of through-the-wall radar imaging (TWRI). This technique can be accomplished through nonlinear methods, including convex programming and greedy iterative algorithms. However, such (nonlinear) methods increase the computational cost at the sensing and reconstruction stages, thus limiting the application of TWRI in delicate practical tasks (e.g. military operations and rescue missions) that demand fast response times. Motivated by this limitation, the current work introduces the use of a numerical optimization algorithm, called Limited Memory Broyden–Fletcher–Goldfarb–Shanno (LBFGS), to the TWRI framework to lower image reconstruction time. LBFGS, a well-known Quasi-Newton algorithm, has traditionally been applied to solve large scale optimization problems. Despite its potential applications, this algorithm has not been extensively applied in TWRI. Therefore, guided by LBFGS and using the Euclidean norm, we employed the regularized least square method to solve the cost function of the TWRI problem. Simulation results show that our method reduces the computational time by 87% relative to the classical method, even under situations of increased number of targets or large data volume. Moreover, the results show that the proposed method remains robust when applied to noisy environment.

scholarly journals Self-Directed Online Machine Learning for Topology Optimization

2021 ◽  
Author(s):  
Changyu Deng ◽  
Yizhou Wang ◽  
Can Qin ◽  
Wei Lu
Keyword(s):  
Topology Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Computational Cost ◽  
Region Of Interest ◽  
Global Optimum ◽  
Training Data ◽  
Computational Time ◽  
Minimization Problems ◽  
Design Variables

Abstract Topology optimization by optimally distributing materials in a given domain requires gradient-free optimizers to solve highly complicated problems. However, with hundreds of design variables or more involved, solving such problems would require millions of Finite Element Method (FEM) calculations whose computational cost is huge and impractical. Here we report a Self-directed Online Learning Optimization (SOLO) which integrates Deep Neural Network (DNN) with FEM calculations. A DNN learns and substitutes the objective as a function of design variables. A small number of training data is generated dynamically based on the DNN's prediction of the global optimum. The DNN adapts to the new training data and gives better prediction in the region of interest until convergence. Our algorithm was tested by compliance minimization problems and fluid-structure optimization problems. It reduced the computational time by 2 ~ 5 orders of magnitude compared with directly using heuristic methods, and outperformed all state-of-the-art algorithms tested in our experiments. This approach enables solving large multi-dimensional optimization problems.

Design optimization of a high-sensitive absolute micro-pressure sensor

Sensor Review ◽  
2014 ◽  
Vol 34 (3) ◽  
pp. 312-318 ◽  
Author(s):  
Zhongliang Yu ◽  
Yulong Zhao ◽  
Lili Li ◽  
Cun Li ◽  
Xiawei Meng ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
Optimization Problems ◽  
High Sensitivity ◽  
Optimization Method ◽  
Structure Design ◽  
Content Type ◽  
Design And Optimization ◽  
Nonlinear Fitting ◽  
Silicon Bulk ◽  
Dynamic Performances

Purpose – The purpose of this study is to develop a piezoresistive absolute micro-pressure sensor for altimetry. For this application, both high sensitivity and high overload resistance are required. To develop a piezoresistive absolute micro-pressure sensor for altimetry, both high sensitivity and high-overload resistance are required. The structure design and optimization are critical for achieving the purpose. Besides, the study of dynamic performances is important for providing a solution to improve the accuracy under vibration environments. Design/methodology/approach – An improved structure is studied through incorporating sensitive beams into the twin-island-diaphragm structure. Equations about surface stress and deflection of the sensor are established by multivariate fittings based on the ANSYS simulation results. Structure dimensions are determined by MATLAB optimization. The silicon bulk micromachining technology is utilized to fabricate the sensor prototype. The performances under both static and dynamic conditions are tested. Findings – Compared with flat diaphragm and twin-island-diaphragm structures, the sensor features a relatively high sensitivity with the capacity of suffering atmosphere due to the introduction of sensitive beams and the optimization method used. Originality/value – An improved sensor prototype is raised and optimized for achieving the high sensitivity and the capacity of suffering atmosphere simultaneously. A general optimization method is proposed based on the multivariate fitting results. To simplify the calculation, a method to linearize the nonlinear fitting and optimization problems is presented. Moreover, a differential readout scheme attempting to decrease the dynamic interference is designed.

Accuracy evaluation of three-dimensional FE analysis based on nonconforming voxel element

2013 ◽  
Vol 33 (1/2) ◽  
pp. 181-190
Author(s):  
Takahiro Sato ◽  
Kota Watanabe ◽  
Hajime Igarashi
Keyword(s):  
Mesh Generation ◽  
Computational Cost ◽  
Three Dimensional ◽  
Fe Analysis ◽  
Computational Time ◽  
Accuracy Evaluation ◽  
Computational Accuracy ◽  
Content Type ◽  
3D Mesh ◽  
3D Shape

Purpose – Three-dimensional (3D) mesh generation for shape optimizations needs long computational time. This makes it difficult to perform 3D shape optimizations. The purpose of this paper is to present a new meshing method with light computational cost for 3D shape optimizations. Design/methodology/approach – This paper presents a new meshing method on the basis of nonconforming voxel finite element method. The 3D mesh generation is performed with light computational cost keeping the computational accuracy. Findings – It is shown that the computational cost for 3D mesh generation can be reduced without deteriorating numerical accuracy in the FE analysis. It is reported the performance of the present method. Originality/value – The validity of the nonconforming voxel elements is tested to apply it to the optimization of 3D optimizations.

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