scholarly journals An Entropy Weight-Based Lower Confidence Bounding Optimization Approach for Engineering Product Design

2020 ◽  
Vol 10 (10) ◽  
pp. 3554 ◽  
Author(s):  
Jiachang Qian ◽  
Jiaxiang Yi ◽  
Jinlan Zhang ◽  
Yuansheng Cheng ◽  
Jun Liu
Keyword(s):  
Optimization Design ◽  
Optimal Solution ◽  
Kriging Model ◽  
Optimization Approach ◽  
Adaptive Optimization ◽  
Entropy Weight ◽  
Engineering Product ◽  
Lower Confidence ◽  
Different Dimensions ◽  
Adaptive Kriging

The optimization design of engineering products involving computationally expensive simulation is usually a time-consuming or even prohibitive process. As a promising way to relieve computational burden, adaptive Kriging-based design optimization (AKBDO) methods have been widely adopted due to their excellent ability for global optimization under limited computational resource. In this paper, an entropy weight-based lower confidence bounding approach (EW-LCB) is developed to objectively make a trade-off between the global exploration and the local exploitation in the adaptive optimization process. In EW-LCB, entropy theory is used to measure the degree of the variation of the predicted value and variance of the Kriging model, respectively. Then, an entropy weight function is proposed to allocate the weights of exploration and exploitation objectively and adaptively based on the values of information entropy. Besides, an index factor is defined to avoid the sequential process falling into the local regions, which is associated with the frequencies of the current optimal solution. To demonstrate the effectiveness of the proposed EW- LCB method, several numerical examples with different dimensions and complexities and the lightweight optimization design problem of an underwater vehicle base are utilized. Results show that the proposed approach is competitive compared with state-of-the-art AKBDO methods considering accuracy, efficiency, and robustness.

scholarly journals Hybrid optimization design approach of asymmetric base-isolation coupling system for twin buildings

2021 ◽  
pp. 146134842199296
Author(s):  
Wonsuk Park ◽  
Seung-Yong Ok
Keyword(s):  
Vibration Control ◽  
Optimization Design ◽  
Base Isolation ◽  
Optimization Technique ◽  
Optimal Solution ◽  
Hybrid Optimization ◽  
Optimization Approach ◽  
Efficient Design ◽  
Coupling System ◽  
Twin Buildings

This study proposes a new configuration of asymmetric base-isolation coupling system for the vibration control of twin buildings, and also presents an efficient design method of using a hybrid optimization technique integrated with preference-based dimensionality reduction technique. The purpose of the proposed optimization approach is to guarantee the compromise optimal solution of well-balancing the mutually conflicting design objectives. In order to demonstrate the proposed approach, the adjacent 20-story twin buildings subjected to earthquake excitations were adopted as target buildings and it was verified through numerical examples that the proposed optimization technique can successfully find the optimal solution to achieve various design objectives in a balanced manner. The seismic performance was also compared with the existing different-story connection system with uniform distribution of dampers. The comparative results of the seismic performances between two systems clearly demonstrate that the proposed system can achieve great performance improvement over the existing system while maintaining balanced design preferences. Thus, it can be concluded that the proposed system can be a very effective system for the vibration control problem of the twin buildings.

Efficient Optimization Design Method Using Kriging Model

2005 ◽  
Vol 42 (5) ◽  
pp. 1375-1375 ◽  
Author(s):  
Shinkyu Jeong ◽  
Mitsuhiro Murayama ◽  
Kazuomi Yamamoto
Keyword(s):  
Optimization Design ◽  
Design Method ◽  
Kriging Model

AUTOMATED SYNTHESIS OF BROADBAND MATCHING AND FILTERING DEVICES

2018 ◽  
pp. 48-52
Author(s):  
S. V. Popov ◽  
G. N. Devyatkov
Keyword(s):  
Transmission Lines ◽  
Synthesis Method ◽  
Optimal Solution ◽  
Complex Problem ◽  
Matching Problem ◽  
Engineering Product ◽  
Second Stage ◽  
Automated Method ◽  
Broadband Matching ◽  
Element Base

When designing radioelectronic devices, that are included in the composition of various systems, it is important to solve broadband matching problem and filtering problem. However, usually these problems are separated and not considered together. Moreover, the synthesis of filters does not take into account the behavior of impedances of the generator and the load in the stopbands. The solution of the complex problem is actual, since it allows expanding the functionality of the device, which can greatly simplify the construction of the radio engineering product. It should be noted that in the known literature solution of this problem in such a formulation is not considered. The aim of the work is to develop a synthesis method and algorithm of broadband devices that connect arbitrary immitances of the generator and the load, and these devices should perform simultaneously functions of both matching and filtering in reactive lumped electric element base and in distributed electric element base, limited only by transmission lines with T-waves. In this paper, a two-stage automated method of synthesis presented here stage allows at the first to adequately find a good initial solution to the posed problem (determining structure and parameters of the broadband matching and filtering quadrupole), in the second stage this approach allows to find the optimal solution to the complex problem, taking into account the constraints on physical and circuit realizability. In this work, the synthesis of broadband matching and filtering devices in lumped and distributed electrical element basis is carried out, and these devices connect complex impedances of the source and the load. The characteristics of the devices obtained after the synthesis show that the solution of the complex problem of matching and filtering gives a significant improvement in filtering properties with small losses in the level of transmitted power.

scholarly journals Exploring the application of machine learning to the assembly line feeding problem

2021 ◽  
Author(s):  
Moretti Emilio ◽  
Tappia Elena ◽  
Limère Veronique ◽  
Melacini Marco
Keyword(s):  
Machine Learning ◽  
Assembly Line ◽  
Optimal Solution ◽  
Product Variety ◽  
Supervised Machine Learning ◽  
Assembly System ◽  
Optimization Approach ◽  
Feeding Problem ◽  
Managerial Implications ◽  
Multiple Feeding

AbstractAs a large number of companies are resorting to increased product variety and customization, a growing attention is being put on the design and management of part feeding systems. Recent works have proved the effectiveness of hybrid feeding policies, which consist in using multiple feeding policies in the same assembly system. In this context, the assembly line feeding problem (ALFP) refers to the selection of a suitable feeding policy for each part. In literature, the ALFP is addressed either by developing optimization models or by categorizing the parts and assigning these categories to policies based on some characteristics of both the parts and the assembly system. This paper presents a new approach for selecting a suitable feeding policy for each part, based on supervised machine learning. The developed approach is applied to an industrial case and its performance is compared with the one resulting from an optimization approach. The application to the industrial case allows deepening the existing trade-off between efficiency (i.e., amount of data to be collected and dedicated resources) and quality of the ALFP solution (i.e., closeness to the optimal solution), discussing the managerial implications of different ALFP solution approaches and showing the potential value stemming from machine learning application.

scholarly journals Strategies for Improving Optimal Positioning of Quality Sensors in Urban Drainage Systems for Non-Conservative Contaminants

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 934
Author(s):  
Mariacrocetta Sambito ◽  
Gabriele Freni
Keyword(s):  
Treatment Plant ◽  
Optimal Solution ◽  
Monitoring Network ◽  
Computational Effort ◽  
Bayesian Optimization ◽  
Urban Drainage ◽  
Optimization Approach ◽  
Sensor Location ◽  
Contamination Source ◽  
Monitoring Point

In the urban drainage sector, the problem of polluting discharges in sewers may act on the proper functioning of the sewer system, on the wastewater treatment plant reliability and on the receiving water body preservation. Therefore, the implementation of a chemical monitoring network is necessary to promptly detect and contain the event of contamination. Sensor location is usually an optimization exercise that is based on probabilistic or black-box methods and their efficiency is usually dependent on the initial assumption made on possible eligibility of nodes to become a monitoring point. It is a common practice to establish an initial non-informative assumption by considering all network nodes to have equal possibilities to allocate a sensor. In the present study, such a common approach is compared with different initial strategies to pre-screen eligible nodes as a function of topological and hydraulic information, and non-formal 'grey' information on the most probable locations of the contamination source. Such strategies were previously compared for conservative xenobiotic contaminations and now they are compared for a more difficult identification exercise: the detection of nonconservative immanent contaminants. The strategies are applied to a Bayesian optimization approach that demonstrated to be efficient in contamination source location. The case study is the literature network of the Storm Water Management Model (SWMM) manual, Example 8. The results show that the pre-screening and ‘grey’ information are able to reduce the computational effort needed to obtain the optimal solution or, with equal computational effort, to improve location efficiency. The nature of the contamination is highly relevant, affecting monitoring efficiency, sensor location and computational efforts to reach optimality.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

scholarly journals Improving Energy Efficiency Fairness of Wireless Networks: A Deep Learning Approach

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4300 ◽  
Author(s):  
Hoon Lee ◽  
Han Seung Jang ◽  
Bang Chul Jung
Keyword(s):  
Energy Efficiency ◽  
Wireless Networks ◽  
Deep Learning ◽  
Power Control ◽  
Control Method ◽  
Optimal Solution ◽  
Control Policy ◽  
Maximization Problem ◽  
Optimization Approach ◽  
Conventional Optimization

Achieving energy efficiency (EE) fairness among heterogeneous mobile devices will become a crucial issue in future wireless networks. This paper investigates a deep learning (DL) approach for improving EE fairness performance in interference channels (IFCs) where multiple transmitters simultaneously convey data to their corresponding receivers. To improve the EE fairness, we aim to maximize the minimum EE among multiple transmitter–receiver pairs by optimizing the transmit power levels. Due to fractional and max-min formulation, the problem is shown to be non-convex, and, thus, it is difficult to identify the optimal power control policy. Although the EE fairness maximization problem has been recently addressed by the successive convex approximation framework, it requires intensive computations for iterative optimizations and suffers from the sub-optimality incurred by the non-convexity. To tackle these issues, we propose a deep neural network (DNN) where the procedure of optimal solution calculation, which is unknown in general, is accurately approximated by well-designed DNNs. The target of the DNN is to yield an efficient power control solution for the EE fairness maximization problem by accepting the channel state information as an input feature. An unsupervised training algorithm is presented where the DNN learns an effective mapping from the channel to the EE maximizing power control strategy by itself. Numerical results demonstrate that the proposed DNN-based power control method performs better than a conventional optimization approach with much-reduced execution time. This work opens a new possibility of using DL as an alternative optimization tool for the EE maximizing design of the next-generation wireless networks.

Synthesis and Numerical Analysis of Compliant devices – A Topology Optimization Approach for Mechanisms and Robotic Systems

2021 ◽  
Author(s):  
Premanand Sathyanarayanamurthi ◽  
ARUNKUMAR GOPAL
Keyword(s):  
Topology Optimization ◽  
Optimization Design ◽  
Rectangular Domain ◽  
Dynamic State ◽  
Optimization Approach ◽  
Design And Development ◽  
Design Synthesis ◽  
Final Destination ◽  
Prototype Development ◽  
External Forces

Abstract The Topology Optimization design invariably shall be used in various applications like Aerojet designs, Aircraft Engineering designs and innovative systems for improving the efficiency of structure. The paper emphasizes more on general Topology Optimization design for a rectangular domain. The domain numerically analyzed with defined geometry setting and defined boundary conditions for finding the Stress and displacement. In this Topology Optimization Design synthesis, the result is suitable volume and mass reduction in the Aerojet application parts which further can be taken for Prototype development in 3D printing and experimentally test with safety characteristics and compares Objective functions chosen for design and development. The design can be used for other various automotive and aerospace devices based on deformation level and application of external forces. The Final destination of this design and development ends with passing Fatigue Endurance test cycle test pass condition in Aerojet and automotive vehicles in static and dynamic state.

A Systematic Optimization Design Method for Thermal Management of Passenger Vehicles

2021 ◽  
pp. 1-27
Author(s):  
Jie Zhang ◽  
Qidong Wang ◽  
Han Zhang ◽  
Min Zhang ◽  
Jianwei Lin
Keyword(s):  
Prediction Model ◽  
Surface Temperature ◽  
Thermal Management ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Optimal Solution ◽  
Optimization Method ◽  
Drive Shaft ◽  
Management Problem ◽  
Systematic Optimization

Abstract In this study, a systematic optimization method for the thermal management problem of passenger vehicle was proposed. This article addressed the problem of the drive shaft sheath surface temperature exceeded allowable value. Initially, the causes and initial measures of the thermal problem were studied through computational fluid dynamics (CFD) simulation. Furthermore, the key measures and the relevant parameters were determined through Taguchi method and significance analysis. A prediction model between the parameters and optimization objective was built by radial basis function neural network (RBFNN). Finally, the prediction model and particle swarm optimization (PSO) algorithm were combined to calculate the optimal solution, and the optimal solution was selected for simulation and experiment verification. Experiment results indicated that this method reduced the drive shaft sheath surface temperature promptly, the decreasing amplitude was 22%, which was met the experimental requirements.

scholarly journals Mechanism reliability and sensitivity analysis of landing gear under multiple failure modes

2021 ◽  
Vol 39 (1) ◽  
pp. 46-54
Author(s):  
Changcong Zhou ◽  
Mengyao Ji ◽  
Yishang Zhang ◽  
Fuchao Liu ◽  
Haodong Zhao
Keyword(s):  
Failure Probability ◽  
Failure Modes ◽  
Kriging Model ◽  
Landing Gear ◽  
Multiple Failure Modes ◽  
Analysis And Design ◽  
Extension System ◽  
Extension Mechanism ◽  
Assembly Error ◽  
Adaptive Kriging

For a certain type of aircraft landing gear retraction-extension mechanism, a multi-body dynamic simulation model is established, and the time-dependent curves of force and angle are obtained. Considering the random uncertainty of friction coefficient, assembly error, and the change of hinge wear under different retraction times, the reliability model is built including three failure modes of landing gear, i.e. blocking failure, positioning failure and accuracy failure. Based on the adaptive Kriging model, the reliability and sensitivity of retraction-extension system under the condition of single failure mode and multiple failure modes in series are analyzed, and the rule of reliability and sensitivity changing with the number of operations is given. The results show that the system failure probability of landing gear mechanism tends to decrease first and then increase when considering the given information of random factors, and the influences of random factors on the failure probability vary with the number of operations. This work provides a viable tool for the reliability analysis and design of landing gear mechanisms.

Sign in / Sign up

Export Citation Format

Share Document