An Integrated Framework for Optimization Under Uncertainty Using Inverse Reliability Strategy

2004 ◽  
Vol 126 (4) ◽  
pp. 562-570 ◽  
Author(s):  
Xiaoping Du ◽  
Agus Sudjianto ◽  
Wei Chen
Keyword(s):  
Combustion Engine ◽  
Search Algorithm ◽  
Traditional Approach ◽  
Optimization Under Uncertainty ◽  
Probabilistic Constraints ◽  
Reliability Design ◽  
Integrated Framework ◽  
Design Objective ◽  
Reliability Assessments ◽  
Objective Robustness

In this work, we propose an integrated framework for optimization under uncertainty that can bring both the design objective robustness and the probabilistic design constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective robustness and probabilistic constraints. The percentile formulation for objective robustness provides us an accurate evaluation of the variation of an objective performance and a probabilistic measurement of the robustness. We can obtain more reasonable compound noise combinations for a robust design objective compared to using the traditional approach proposed by Taguchi. The proposed formulation is very efficient to solve since it only needs to evaluate the constraint functions at the required reliability levels. The other major development of this work is a new search algorithm for the Most Probable Point of Inverse Reliability (MPPIR) that can be used to efficiently evaluate percentile performances for both robustness and reliability assessments. Multiple strategies are employed in the MPPIR search, including using the steepest ascent direction and an arc search. The algorithm is applicable to general non-concave and non-convex performance functions of random variables following any continuous distributions. The effectiveness of the MPPIR search algorithm is verified using example problems. Overall, an engineering example on integrated robust and reliability design of a vehicle combustion engine piston is used to illustrate the benefits of our proposed method.

An Integrated Framework for Probabilistic Optimization Using Inverse Reliability Strategy

2003 ◽  
Author(s):  
Xiaoping Du ◽  
Agus Sudjianto ◽  
Wei Chen
Keyword(s):  
Combustion Engine ◽  
Search Algorithm ◽  
Probabilistic Constraints ◽  
Probabilistic Measure ◽  
Reliability Design ◽  
Probabilistic Optimization ◽  
Integrated Framework ◽  
Engine Piston ◽  
Major Development ◽  
Objective Robustness

In this work, we propose an integrated framework for probabilistic optimization that can bring both the design objective robustness and the probabilistic constraints into account. The fundamental development of this work is the employment of an inverse reliability strategy that uses percentile performance for assessing both the objective robustness and probabilistic constraints. The percentile formulation for objective robustness provides an accurate probabilistic measure for robustness and more reasonable compound noise combinations. For the probabilistic constraints, compared to a traditional probabilistic model, the proposed formulation is more efficient since it only evaluates the constraint functions at the required reliability levels. The other major development of this work is a new search algorithm for the Most Probable Point of Inverse Reliability (MPPIR) that can be used to efficiently evaluate the performance robustness and percentile performance in the proposed formulation. Multiple techniques are employed in the MPPIR search, including the steepest decent direction and an arc search. The algorithm is applicable to general non-concave and non-convex functions of system performance with random variables following any continuous distributions. The effectiveness of the MPPIR search algorithm is verified using example problems. Overall, an engineering example on integrated robust and reliability design of a vehicle combustion engine piston is used to illustrate the benefits of the proposed method.

Synergetic Control System of Hybrid Power Plant

2018 ◽  
Vol 19 (10) ◽  
pp. 627-632
Author(s):  
A. A. Kolesnikov ◽  
S. D. Kaliy ◽  
I. A. Radionov ◽  
O. I. Yakimenko
Keyword(s):  
Power Plant ◽  
Internal Combustion Engine ◽  
Electric Motor ◽  
Permanent Magnets ◽  
Combustion Engine ◽  
Traditional Approach ◽  
Control Object ◽  
Internal Combustion ◽  
Hybrid Power ◽  
Hybrid Power Plant

The problem of control of a hybrid power plant of a car consisting of an internal combustion engine, a synchronous electric motor with permanent magnets and a synchronous generator is considered. The formation of the control effect is carried out taking into account the connection of the above objects with each other with the help of planetary transmission. The mathematical models of the three listed engines are nonlinear with several control channels. In addition, the principle of the hybrid power plant requires the simultaneous operation of these engines and, accordingly, the construction of the necessary interrelated control actions. To synthesize the laws of vector control of a hybrid power plant, the method of analytical construction of aggregated regulators (ADAR) is used. Within the framework of this method, it is possible to work with a complete nonlinear control object model. Unlike the traditional approach of constructing a separate stabilizing control for each control channel, this method uses co-control over all variables to transfer the object to the desired state. In this case,for a number of variants of control algorithms, the communication between the control channels is carried out not indirectly, through the control object, but directly formed in the regulator. In addition, the control law takes into account unknown external disturbances, which were compensated using the principle of integral adaptation. In this paper, one of the modes of operation of a hybrid power plant is shown during the acceleration of the car. First, only the electric motor works, as the car accelerates, the internal combustion engine is connected, and at high speeds only the internal combustion engine works. This mode of operation of the hybrid power plant allows using both engines in the most convenient range of angular speeds, which leads to an economical fuel consumption and a charge of the storage batteries. In addition, the second electric motor operates in the generator mode and transfers a part of the mechanical moment to recharge the batteries.

Time-Dependent Reliability-Based Robust Design Optimization via Extreme Value Moment Method

2018 ◽  
Author(s):  
Shui Yu ◽  
Zhonglai Wang
Keyword(s):  
Design Optimization ◽  
Reliability Analysis ◽  
Robust Design ◽  
Moment Method ◽  
Time Dependent ◽  
Robust Design Optimization ◽  
Probabilistic Constraints ◽  
Multi Objective Optimization ◽  
Integrated Framework ◽  
Multi Objective

During the product design and development stage, design engineers often encounter reliability and robustness of dynamic uncertain structures. Meanwhile, time-varying and high nonlinear performance are the basic characteristics of reliability analysis and design. Hence, the time-dependent reliability analysis and integrating reliability-based design with robust design become a primary challenge in reliability-based robust design optimization. This paper proposes a multi-objective integrated framework for time-dependent reliability-based robust design optimization and the corresponding algorithms. The multi-objective integrated framework, which minimizes the mean value and coefficient of variation for the objective function at the same time subject to time-dependent probabilistic constraints, is first established. The time-dependent probabilistic constraints are then converted into deterministic constraints using a combination of moment method and the sparse grid based stochastic collocation method. The evolutionary multi-objective optimization algorithm is finally employed for the deterministic multi-objective optimization problem. Several examples are investigated to demonstrate the effectiveness of the proposed method.

Reliability Assessments of Concrete Filled FRP Tube Columns

2013 ◽  
Vol 405-408 ◽  
pp. 731-734
Author(s):  
Ying Wu Zhou ◽  
Feng Xing ◽  
Li Li Sui
Keyword(s):  
Safety Factor ◽  
Coefficient Of Variation ◽  
Reliability Index ◽  
Concrete Strength ◽  
Reliability Design ◽  
Partial Safety Factor ◽  
Highly Sensitive ◽  
Partial Safety Factors ◽  
Reliability Assessments ◽  
Frp Tube

This paper has investigated the reliability of concrete filled FRP tube columns using the FRP confined concrete theory developed recently by the authors. The reliability index of the column is assessed by Monte Carlo method. The importance of the use of partial safety factors of FRP and concrete in the reliability design of concrete filled FRP tube columns is studied. The results indicate that the reliability index of concrete filled FRP tube columns increases remarkably as the FRP partial safety factor increased. It is concluded that the FRP partial safety factor is independent on the coefficient of variation of FRP strength but is highly sensitive to the coefficient of variation of concrete strength especially in the case of low confinement ratio. Considering the actual situation in engineering applications, to reach a target reliability index of 3.5, a partial safety factor of 1.4 is finally recommend for both FRP and concrete.

scholarly journals Optimized support vector regression for drilling rate of penetration estimation

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Asadollah Bodaghi ◽  
Hamid Reza Ansari ◽  
Mahsa Gholami
Keyword(s):  
Support Vector Regression ◽  
Search Algorithm ◽  
Traditional Approach ◽  
Back Propagation ◽  
Petroleum Industry ◽  
Pattern Search ◽  
Operating Conditions ◽  
Support Vector ◽  
Rate Of Penetration ◽  
Drilling Optimization

Abstract In the petroleum industry, drilling optimization involves the selection of operating conditions for achieving the desired depth with the minimum expenditure while requirements of personal safety, environment protection, adequate information of penetrated formations and productivity are fulfilled. Since drilling optimization is highly dependent on the rate of penetration (ROP), estimation of this parameter is of great importance during well planning. In this research, a novel approach called ‘optimized support vector regression’ is employed for making a formulation between input variables and ROP. Algorithms used for optimizing the support vector regression are the genetic algorithm (GA) and the cuckoo search algorithm (CS). Optimization implementation improved the support vector regression performance by virtue of selecting proper values for its parameters. In order to evaluate the ability of optimization algorithms in enhancing SVR performance, their results were compared to the hybrid of pattern search and grid search (HPG) which is conventionally employed for optimizing SVR. The results demonstrated that the CS algorithm achieved further improvement on prediction accuracy of SVR compared to the GA and HPG as well. Moreover, the predictive model derived from back propagation neural network (BPNN), which is the traditional approach for estimating ROP, is selected for comparisons with CSSVR. The comparative results revealed the superiority of CSSVR. This study inferred that CSSVR is a viable option for precise estimation of ROP.

Spacecraft mission design optimization under uncertainty

2016 ◽  
Vol 230 (16) ◽  
pp. 2872-2887 ◽  
Author(s):  
A Jafarsalehi ◽  
HR Fazeley ◽  
M Mirshams
Keyword(s):  
Design Optimization ◽  
Local Level ◽  
Electrical Power ◽  
Low Earth Orbit ◽  
Optimization Under Uncertainty ◽  
System Level ◽  
Probabilistic Constraints ◽  
Mission Design ◽  
Mass Model ◽  
System Design Optimization

The design of space systems is a complex and multidisciplinary process. In this study, two deterministic and nondeterministic approaches are applied to the system design optimization of a spacecraft which is actually a small satellite in low Earth orbit with remote sensing mission. These approaches were then evaluated and compared. Different disciplines such as mission analysis, payload, electrical power supply, mass model, and launch manifest were properly combined for further use. Furthermore, genetic algorithm and sequential quadratic programming were employed as the system-level and local-level optimizers. The main optimization objective of this study is to minimize the resolution of the satellite imaging payload while there are several constraints. A probabilistic analysis was performed to compare the results of the deterministic and nondeterministic approaches. Analysis of the results showed that the deterministic approaches may lead to an unreliable design (because of leaving little or no room for uncertainties), while using the reliability-based multidisciplinary design optimization architecture, all probabilistic constraints were satisfied.

scholarly journals An Efficient Integrated Framework of Reliability-Based Robust Design Optimization for Computation-Intensive Structures

2021 ◽  
Author(s):  
xiongming lai ◽  
Ju Huang ◽  
Cheng Wang ◽  
Yong Zhang
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Optimization Problems ◽  
Trust Region Method ◽  
Performance Measure ◽  
Robust Design Optimization ◽  
Probabilistic Constraints ◽  
Engineering Structures ◽  
Integrated Framework ◽  
Finite Element Software

Abstract When carrying out robust design optimization for complex engineering structures, they are computed by finite element software and are always computation-intensive. Aim at this problem, the paper proposes an efficient integrated framework of Reliability-based Robust Design Optimization (RBRDO). Firstly, the conventional RBRDO problem is changed as percentile form, that is, the improved percentile formulation of computing the objective robustness and probabilistic constraints is presented by resorting to the employment of Performance Measure Approach (PMA). Secondly, the above improved RBRDO problem is simplified by a series of new approximation methods due to the need of reducing computation. An efficient approximation method is proposed to estimate PMA functions of the RBRDO formulation. Based on it, the above improved RBRDO problem can be transformed into a sequence of approximate deterministic sub-optimization problems, whose objective function and constraints are expressed as the approximate explicit form only in relation to the design variables. Furthermore, use the trust region method to solve the above sequence of sub-optimization. Lastly, several examples are used to demonstrate the effectiveness and efficiency of the proposed method.

Jacket Reliability Design Considering Interacting Limit States

2003 ◽  
Author(s):  
B. F. Ronalds ◽  
R. Pinna ◽  
S. P. Ryan ◽  
J. A. Riordan ◽  
T. M. Radic ◽  
...  
Keyword(s):  
Shallow Water ◽  
Progressive Collapse ◽  
Potential Importance ◽  
Strength Ratio ◽  
Limit States ◽  
Quality Requirements ◽  
Reliability Design ◽  
Jacket Structures ◽  
Reliability Assessments ◽  
Structural Configurations

The reliability of jacket structures is addressed by considering inter-relationships between ultimate strength, fatigue and progressive collapse limit states, for both intact and damaged structural configurations and a range of defect and collapse events, including accidents and gross errors. Simple robustness criteria for use in design are suggested based on the ratio of damaged-to-intact probabilities of failure, a progressive collapse indicator and damaged strength ratio (DSR) values. Their practicality is illustrated by detailed reliability assessments of two quite different jacket structures — a major 6-leg platform and a shallow water tripod. The strong interdependence between robustness and quality requirements is demonstrated, as is the potential importance of fatigue-driven progressive collapse for certain structures.

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