Robust optimization of powertrain mounting system based on interval analysis

2015 ◽  
Author(s):  
C. Li ◽  
B.L. Zhang ◽  
Z.Z. Cong ◽  
H. Jing ◽  
J.D. Bao
Keyword(s):  
Robust Optimization ◽  
Interval Analysis

A Robust Aerodynamic Optimization Design for Airfoil Based on Interval Uncertainty Analysis Method

2019 ◽  
Author(s):  
Xin Song ◽  
Guannan Zheng ◽  
Guowei Yang
Keyword(s):  
Robust Optimization ◽  
Interval Analysis ◽  
Optimization Design ◽  
Aerodynamic Force ◽  
Interval Uncertainty ◽  
Upper And Lower Bounds ◽  
Analysis Method ◽  
Geometric Uncertainties ◽  
Interval Analysis Method ◽  
Robust Optimization Design

Abstract Uncertainties will make aircraft deviate from the designed condition, resulting in the decrease in aerodynamic performance and even destruction. This paper presents a fast nonlinear interval analysis method considering geometric uncertainties. DFFD method is used to parameterize the airfoil shape, and the Kriging model for aerodynamic force and uncertainty variables is optimized by PSO algorithm to find the upper and lower bounds of the objective interval. The effects of geometric uncertainties on NACA0012 airfoil are analyzed using the above method. And then, a robust optimization design method is established based on the interval analysis method. FFD method is used to produce the deterministic design variables and the order relation of interval number is employed to transform the uncertain optimization to deterministic multi-objective optimization which is solved by MOPSO based on Pareto entropy. The robust optimization design is implemented for the symmetrical airfoil with the drag objective under geometric uncertainties and thickness constraint, and the results are compared with the deterministic optimization to validate the effectiveness of the developed method.

Robust optimization of EMU brake module based on interval analysis

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yonghua Li ◽  
Hao Yin ◽  
Qing Xia
Keyword(s):  
Robust Optimization ◽  
Interval Analysis ◽  
Optimization Model ◽  
Maximum Stress ◽  
Interval Order ◽  
Module Structure ◽  
Content Type ◽  
Design Variables ◽  
Stress Fluctuation ◽  
Design Requirements

PurposeThis study aims to research the influence of non-probabilistic design variables on interval robust optimization of electric multiple units (EMU) brake module, therefore obtain the reasonable of design variables of the EMU brake module.Design/methodology/approachA robust optimization model of the EMU brake module based on interval analysis is established. This model also considers the dimension tolerance of design variables, and it uses symmetric tolerance to describe the uncertainty of design variables. The interval order relation and possibility degree of interval number are employed to deal with the uncertainty of objective function and constraint condition, respectively. On this basis, a multiobjective robust optimization model in view of interval analysis is established and applied to the robust optimization of the EMU brake module.FindingsCompared with the traditional method and the method proposed in the reference, the maximum stress fluctuation of the EMU brake module structure is smaller after using the method proposed in this paper, which indicates that the robustness of the maximum stress of the structure has been improved. In addition, the weight and strength of the structure meet the design requirements. It shows that this method and model introduced in this research have certain feasibility.Originality/valueThis study is the first attempt to apply the robust optimization model based on interval analysis to the optimization of EMU structure and obtain the optimal solution set that meets the design requirements. Therefore, this study provides an idea for nonprobabilistic robust optimization of the EMU structure.

Network Congestion Minimization Models Based on Robust Optimization

2018 ◽  
Vol E101.B (3) ◽  
pp. 772-784 ◽  
Author(s):  
Bimal CHANDRA DAS ◽  
Satoshi TAKAHASHI ◽  
Eiji OKI ◽  
Masakazu MURAMATSU
Keyword(s):  
Robust Optimization ◽  
Network Congestion

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

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