scholarly journals Information Matrix-Based Adaptive Sampling in Hull Form Optimisation

2021 ◽  
Vol 9 (9) ◽  
pp. 973
Author(s):  
Xuyu Ouyang ◽  
Haichao Chang ◽  
Baiwei Feng ◽  
Zuyuan Liu ◽  
Chengsheng Zhan ◽  
...  
Keyword(s):  
Adaptive Sampling ◽  
Information Matrix ◽  
Gaussian Function ◽  
Accurate Description ◽  
Latin Hypercube Design ◽  
Mathematical Functions ◽  
Hull Form ◽  
Approximate Models ◽  
Design Variables ◽  
Function Information

Hull form optimisation involves challenges such as large design spaces, numerous design variables, and high nonlinearity. Therefore, optimisation that only use global approximate models alone cannot yield desirable results. An information matrix-based method is proposed for dynamically embedded local approximate models (IM-DEAM) in this paper, which uses the Gaussian-function information matrix to extract one or more subspaces for additional sampling and a Latin hypercube design (LHD) for adaptive sampling. In addition, to prevent overfitting by global approximate models in some spaces because of the uneven distribution of the samples, local approximate models are embedded in the subspaces identified for additional sampling to enable accurate description of subspaces. The effectiveness and robustness of the method are validated and analysed by applying the proposed method to optimise mathematical functions and the hull form of the DTMB 5415. The results demonstrate that the proposed method is effective for improving the accuracies and can produce reliable optimisation results.

Beyond the Known: Detecting Novel Feasible Domains Over an Unbounded Design Space

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Wei Chen ◽  
Mark Fuge
Keyword(s):  
Real World ◽  
Adaptive Sampling ◽  
Design Space ◽  
Fundamental Problem ◽  
Domain Boundary ◽  
Sampling Technique ◽  
Standard Test ◽  
Worst Case ◽  
Trade Offs ◽  
Design Variables

To solve a design problem, sometimes it is necessary to identify the feasible design space. For design spaces with implicit constraints, sampling methods are usually used. These methods typically bound the design space; that is, limit the range of design variables. But bounds that are too small will fail to cover all possible designs, while bounds that are too large will waste sampling budget. This paper tries to solve the problem of efficiently discovering (possibly disconnected) feasible domains in an unbounded design space. We propose a data-driven adaptive sampling technique—ε-margin sampling, which learns the domain boundary of feasible designs and also expands our knowledge on the design space as available budget increases. This technique is data-efficient, in that it makes principled probabilistic trade-offs between refining existing domain boundaries versus expanding the design space. We demonstrate that this method can better identify feasible domains on standard test functions compared to both random and active sampling (via uncertainty sampling). However, a fundamental problem when applying adaptive sampling to real world designs is that designs often have high dimensionality and thus require (in the worst case) exponentially more samples per dimension. We show how coupling design manifolds with ε-margin sampling allows us to actively expand high-dimensional design spaces without incurring this exponential penalty. We demonstrate this on real-world examples of glassware and bottle design, where our method discovers designs that have different appearance and functionality from its initial design set.

Ship Hull Form Optimization Using Artificial Bee Colony Algorithm

2014 ◽  
Author(s):  
Fuxin Huang ◽  
Lijue Wang ◽  
Chi Yang
Keyword(s):  
Drag Reduction ◽  
Artificial Bee Colony ◽  
Interpolation Method ◽  
Ship Hull ◽  
Mathematical Functions ◽  
Hull Form ◽  
Abc Algorithm ◽  
Bee Colony ◽  
Hull Forms ◽  
Fast Flow

In this paper, artificial bee colony (ABC) algorithms are introduced to optimize ship hull forms for reduced drag. Two versions of ABC algorithm are used: one is the basic ABC algorithm, and the other is an improved artificial bee colony (IABC) algorithm. A recently developed fast flow solver based on the Neumann-Michell theory is used to evaluate the drag of the ship in the optimization process. The ship hull surface is represented by discrete triangular panels and modified using radial basis function interpolation method. The developed optimization algorithms are first validated by benchmark mathematical functions with different dimensions. They are then applied to the optimization of DTMB Model 5415 for reduced drag. Two optimal hull forms are obtained by the ABC and the IABC algorithms. A large drag reduction is obtained by both of the algorithms. The optimal hull form obtained by the IABC algorithm has larger drag reduction than that of the hull form from the ABC algorithm. The results show that two ABC algorithms can be used for optimizing ship hull forms and the IABC algorithm has better performance than the ABC algorithm for the tested case in ship hull form optimization.

Stochastic Optimization of Nonlinear Energy Sinks Using Resonance-Based Clustering

2016 ◽  
Author(s):  
Ethan Boroson ◽  
Samy Missoum
Keyword(s):  
Stochastic Optimization ◽  
Adaptive Sampling ◽  
Dimensional Space ◽  
Optimization Method ◽  
Expected Value ◽  
Design Parameters ◽  
Support Vector ◽  
Design Variables ◽  
Energy Dissipated ◽  
Nonlinear Energy

Nonlinear energy sinks (NESs) are promising devices for achieving passive vibration mitigation. Unlike traditional tuned mass dampers (TMDs), NESs, characterized by nonlinear stiffness properties, are not tuned to specific frequencies and absorb energy over a wider range of frequencies. NES efficiency is achieved through time-limited resonances, leading to the capture and dissipation of energy. However, the efficiency with which a NES dissipates energy is highly dependent on design parameters and loading conditions. In fact, it has been shown that a NES can exhibit a near-discontinuous efficiency. Thus, NES optimal design must account for uncertainty. The premise of the stochastic optimization method proposed is the segregation of efficiency regions separated by discontinuities in potentially high dimensional space. Clustering, support vector machine classification, and dedicated adaptive sampling constitute the basic techniques for maximizing the expected value of NES efficiency. Previous works depended solely on the ratio of energy dissipated by the NES for clustering. This work also includes information about the type of m:p resonances present. Three examples of optimization for the maximization of the expected value of efficiency for NESs subjected to transient loading are presented. The optimization accounts for both design variables with uncertainty and aleatory variables to characterize loading.

Optimization of tire tread pattern based on flow characteristics to improve hydroplaning resistance

2020 ◽  
Vol 234 (13) ◽  
pp. 2961-2974
Author(s):  
Haichao Zhou ◽  
Zhen Jiang ◽  
Baiyu Jiang ◽  
Hao Wang ◽  
Guolin Wang ◽  
...  
Keyword(s):  
Numerical Modeling ◽  
Simulated Annealing ◽  
Water Flow ◽  
Surrogate Model ◽  
Lift Force ◽  
Simulated Annealing Algorithm ◽  
Latin Hypercube Design ◽  
Water Flow Velocity ◽  
Design Variables ◽  
Annealing Algorithm

Tire tread pattern is a crucial parameter to prevent hydroplaning. In this study, numerical modeling was used to investigate tire hydroplaning based on flow–structure interaction. The empirical model of hydroplaning speed published in the literature was used to validate the computational model. Analysis of water flow velocity and turbulent flow energy revealed that lateral grooves of the tire significantly influenced water drainage capacity. Based on the relationship between water flow vector and lateral groove shape, a combination of Kriging surrogate model and simulated annealing algorithm was used to optimize lateral groove design to minimize hydrodynamic lift force. Four geometry parameters of lateral grooves were selected as the design variables. Based on design of experiment principle, 12 simulation cases based on the optimal Latin hypercube design method were used to analyze the influence of design variables on hydrodynamic lift force. The surrogate model was optimized by the simulated annealing algorithm to optimize tire tread pattern. The results indicated that at the same water flow speed, the optimized lateral grooves can reduce hydrodynamic lift force by 14.05% and thus greatly improve safety performance of the tire. This study proves the validity and applicability of using numerical modeling for solving the complex design of tire tread pattern and optimization problem.

The ANSDM Converters Dynamic Range Design Process

2020 ◽  
Vol 10 (3) ◽  
pp. 1-7
Author(s):  
Ryszard Golański ◽  
Juliusz Godek
Keyword(s):  
Adaptive Sampling ◽  
Dynamic Range ◽  
Sampling Interval ◽  
Nonuniform Sampling ◽  
Step Size ◽  
Mathematical Functions ◽  
Coding Efficiency ◽  
Sampling Intervals ◽  
Delta Modulation ◽  
Nonstationary Sources

The results of analytic and simulating works proved that for nonstationary sources, the delta converters with adaptive sampling expose higher coding efficiency than the former proposals, based on uniform sampling methods. The knowledge of the sampling interval range and the algorithm of the Nonuniform Sampling Delta Modulation and Adaptive Nonuniform Sampling Delta Modulation allows finding the necessary number of the sampling intervals and their values that maximizes SNR. The total dynamic range of the ANSDM modulator is the product of the dynamic range both from sampling interval and step size adaptation. Due to the high complexity of the calculations, the ANSDMsoft program was developed to support computing. All computational works were carried out using the Maple environment. Maple allows to solve complex mathematical functions and display their results in a simple way. Most importantly, it supports the LambertW function, used in the computing of NSDM or ANSDM modulators parameters. Graphic illustrations of the NSDM and ANSDM modulator dynamic range as a function of the minimum and maximum sampling frequency are presented.

scholarly journals Design Optimization and Experimental Verification of Permanent Magnet Synchronous Motor Used in Electric Compressors in Electric Vehicles

2020 ◽  
Vol 10 (9) ◽  
pp. 3235 ◽  
Author(s):  
Soo-Whang Baek ◽  
Sang Wook Lee
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Optimization Method ◽  
Synchronous Motor ◽  
Latin Hypercube Design ◽  
Latin Hypercube ◽  
Design Variables ◽  
Efficiency Performance

In this study, a shape design optimization method is proposed to improve the efficiency of a 3 kW permanent magnet synchronous motor (PMSM) used in an electric compressor intended for use in an electric vehicle. The proposed method improves the efficiency performance of the electric compressor by improving the torque characteristics of the initial PMSM model. The dimensions of the rotor were set as the design variables and were chosen to maximize efficiency and reduce cogging torque. During the determination of the design points with conventional Latin hypercube design, the experimental points may be closely related to each other. Therefore, the optimal Latin hypercube design was used to optimally distribute the experimental points evenly and improve the space filling characteristics. The Kriging model was used as an interpolation model to predict the optimal values of the design variables. This allowed the formulation of more accurate prediction models with multiple design variables, complex reactions, or nonlinearities. A genetic algorithm was used to identify the optimal solution for the design variables. It was used to satisfy the objective function and to determine the optimal design variables based on established constraints. The optimal design results obtained based on the proposed shape optimization method were confirmed by finite element analyses. For practical verification, the optimal model of the prototype PMSM of an electric compressor was manufactured, and a 1.5% improvement in its efficiency performance was confirmed based on an experimental dynamometer test.

Optimum Thermal Design of Radiative-Conductive Systems

1972 ◽  
Vol 94 (2) ◽  
pp. 373-380 ◽  
Author(s):  
R. W. Palmquist ◽  
W. A. Beckman
Keyword(s):  
Heat Transfer ◽  
Nonlinear Programming ◽  
Surface Properties ◽  
Thermal Design ◽  
Mathematical Functions ◽  
Design Problems ◽  
Solar Absorptance ◽  
Design Variables ◽  
Physical Limitations ◽  
Conductive System

In applying nonlinear programming to optimization of temperatures in a system with radiation and conduction heat transfer, design requirements on the temperatures are translated into mathematical functions in which the design variables are the radiation surface properties, infrared emmittance and solar absorptance. Physical limitations in the surface properties and design objectives form the constraints of the nonlinear programming problem. A mathematical model of a radiative-conductive system employs a nodal analysis. Radiative heat transfer is treated under the semi-gray assumption and a total exchange factor allows surfaces to be specular-diffuse reflectors. Two types of design problems formulated consider (a) the case in which components of a system must operate within certain temperature limits and (b) a system in which uncertainty in the parameters produces uncertainty in the temperatures.

scholarly journals Designing single-cell experiments to harvest fluctuation noise while rejecting measurement noise

2021 ◽  
Author(s):  
Huy D Vo ◽  
Brian E Munsky
Keyword(s):  
Gene Expression ◽  
Measurement Error ◽  
Single Cell ◽  
Information Matrix ◽  
Experiment Design ◽  
Error Modeling ◽  
Measurement Noise ◽  
Noise Characteristics ◽  
Design Variables ◽  
Gene Expression Heterogeneity

Measurement error is a complicating factor that could reduce or distort the information contained in an experiment. This problem becomes even more serious in the context of experiments to measure single-cell gene expression heterogeneity, in which important quantities such as RNA and protein copy numbers are themselves subjected to the inherent randomness of biochemical reactions. Yet, it is not clear how measurement noise should be managed, in addition to other experiment design variables such as sampling size and frequency, in order to ensure that the collected data provides useful insights on the gene expression mechanism of interest. To address these experiment design challenges, we propose a model-centric framework that makes explicit use of measurement error modeling and Fisher Information Matrix-based criteria to decide between experimental methods. This unified approach not only allows us to see how different noise characteristics affect uncertainty in parameter estimation, but also enables a systematic approach to designing hybrid experiments that combine different measurement methods.

scholarly journals Development of an Efficient Hull Form Design Exploration Framework

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Shinkyu Jeong ◽  
Hyunyul Kim
Keyword(s):  
Data Mining ◽  
Response Surface Methodology ◽  
Shape Optimization ◽  
Response Surface ◽  
Knowledge Extraction ◽  
Wave Drag ◽  
Design Knowledge ◽  
Hull Form ◽  
Design Exploration ◽  
Design Variables

A high-efficiency design exploration framework for hull form has been developed. The framework consists of multiobjective shape optimization and design knowledge extraction. In multiobjective shape optimization, a multiobjective genetic algorithm (MOGA) using the response surface methodology is introduced to achieve efficient design space exploration. As a response surface methodology, the Kriging model, which was developed in the field of spatial statistics and geostatistics, is applied. A new surface modification method using shifting method and radial basis function interpolation is also adopted here to represent various hull forms. This method enables both global and local modifications of hull form with fewer design variables. In design knowledge extraction, two data mining techniques—functional analysis of variance (ANOVA) and self-organizing map (SOM)—are applied to acquire useful design knowledge about a hull form. The present framework has been applied to hull form optimization exploring the minimum wave drag configuration under a wide range of speeds. The results show that the present method markedly reduced the design period. From the results of data mining, it is possible to identify the design variables controlling wave drag performances at different speed regions and their corresponding geometric features.

scholarly journals Application of Improved Particle Swarm Optimisation Algorithm in Hull Form Optimisation

2021 ◽  
Vol 9 (9) ◽  
pp. 955
Author(s):  
Qiang Zheng ◽  
Bai-Wei Feng ◽  
Zu-Yuan Liu ◽  
Hai-Chao Chang
Keyword(s):  
Data Processing ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Resistance Coefficient ◽  
Particle Swarm Optimisation ◽  
Hull Form ◽  
Space Reduction ◽  
Design Variables ◽  
Particle Swarm Optimisation Algorithm ◽  
Improved Pso

The particle swarm optimisation (PSO) algorithm has been widely used in hull form optimisation owing to its feasibility and fast convergence. However, similar to other intelligent algorithms, PSO also has the disadvantages of local premature convergence and low convergence performance. Moreover, optimization data are not used to analyse and reduce the range of values for relevant design variables. Our study aimed to solve these existing problems in the PSO algorithm and improve PSO from four aspects, namely data processing of particle swarm population initialisation, data processing of iterative optimisation, particle velocity adjustment, and particle cross-boundary configuration, in combination with space reduction technology. The improved PSO algorithm was used to optimise the hull form of an engineering vessel at Fn = 0.24 to reduce the wave-making resistance coefficient under static constraints. The results showed that the improved PSO algorithm could effectively improve the optimisation efficiency and reliability of PSO and effectively overcome the drawbacks of the PSO algorithm.

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