Performance comparison of several response surface surrogate models and ensemble methods for water injection optimization under uncertainty

2016 ◽  
Vol 91 ◽  
pp. 19-32 ◽  
Author(s):  
Masoud Babaei ◽  
Indranil Pan
Keyword(s):  
Response Surface ◽  
Water Injection ◽  
Ensemble Methods ◽  
Surrogate Models ◽  
Performance Comparison ◽  
Optimization Under Uncertainty

scholarly journals Advanced gas turbine performance modelling using response surface methods

2018 ◽  
Vol 122 (1258) ◽  
pp. 1871-1883
Author(s):  
V. Seetharama-Yadiyal ◽  
G.D. Brighenti ◽  
P.K. Zachos
Keyword(s):  
Complex System ◽  
Response Surface ◽  
Pressure Ratio ◽  
Surrogate Models ◽  
Experimental Information ◽  
Design Parameters ◽  
Performance Modelling ◽  
Turbine Performance ◽  
High Pressure Level ◽  
Statistical Metrics

ABSTRACTSurrogate models are widely used for dataset correlation. A popular application very frequently shown in public literature is in the field of engineering design where a large number of design parameters are correlated with performance indices of a complex system based on existing numerical or experimental information. Such an approach allows the identification of the key design parameters and their impact on the system’s performance. The generated surrogate model can become part of wider computational platforms and enable optimisation of the complex system without the need to run expensive simulations.In this paper, a number of design point simulations for a combined gas-steam cycle are used to generate a response surface. The generated response surface correlates a range of cycle’s key design parameters with its thermal efficiency while it also enables identification of the optimum overall pressure ratio and the high pressure level of the raised steam across a range of recuperator effectiveness, pinch temperature difference across the heat recovery steam generator and the pressure at the condenser. The accuracy of a range of surrogate models to capture the design space is evaluated using root mean square statistical metrics.

Multi-Objective Optimization of Multistage Centrifugal Pump Based on Surrogate Model

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
Tong Shuiguang ◽  
Zhao Hang ◽  
Liu Huiqin ◽  
Yu Yue ◽  
Li Jinfu ◽  
...  
Keyword(s):  
Response Surface ◽  
Centrifugal Pump ◽  
Calculation Method ◽  
Cfd Simulation ◽  
Surrogate Models ◽  
Hydraulic Loss ◽  
Loss Model ◽  
Design Variables ◽  
Quadratic Response Surface ◽  
Quadratic Response

Abstract In this paper, the hydraulic efficiency optimization calculation method of a ten-stage centrifugal pump is researched. According to the hydraulic loss model, a multi-objective optimization calculation method based on surrogate models is proposed. In order to study the highly nonlinear relationship between key design variables and centrifugal pump external characteristic values, this paper builds the quadratic response surface, the radial basis Gaussian response surface, and Kriging three surrogate models using computer fluid dynamics (CFD) simulation analysis. Two types of calculation models (hydraulic loss model and three surrogate models) combined NSGA-Π genetic algorithm are applied to optimize the key design variables and to find the optimal solution of each model. The accuracy and effectiveness of the efficiency optimization methods based on the two types of calculation models are compared and analyzed. The results show that the calculation method of hydraulic loss model based on the semitheoretical and semi-empirical formula is less time-consuming but inaccurate. In contrast, the optimization method based on surrogate models using CFD simulation is accurate. What's more, comparing the surrogate models, the results based on the complete quadratic response surface model which make the efficiency of the first stage centrifugal pump reach 77.26% are more accurate.

RELIABILITY DESIGN EVALUATION AND OPTIMIZATION OF A NITROGEN GAS BOTTLE USING RESPONSE SURFACE METHOD

2010 ◽  
Vol 17 (02) ◽  
pp. 119-132 ◽  
Author(s):  
P. BHATTACHARJEE ◽  
K. RAMESH KUMAR ◽  
T. A. JANARDHAN REDDY
Keyword(s):  
Design Optimization ◽  
Response Surface ◽  
Response Surface Method ◽  
Structural Reliability ◽  
Operating Conditions ◽  
Optimization Under Uncertainty ◽  
Reliability Based Design Optimization ◽  
Nitrogen Gas ◽  
Reliability Based Design ◽  
Surface Method

Optimization of any aerospace product results in increasing payload capacity of space vehicles. Essentially weight, volume and cost are the main constraints. Design optimization studies for aerospace system are increasingly gaining importance. The problem of optimum design under uncertainty has been formulated as reliability-based design optimization. The reliability based optimization, which includes robustness requirements leads to multi-objective optimization under uncertainty. In this paper Reliability, based design optimization study is carried out under linear constraint optimization to minimize the weight of a nitrogen gas bottle with specified target reliability. Response surface method considering full factorial experiment is used to establish multiple regression equation for induced hoop stress and maximum strain. Necessary data pertaining to design, manufacturing and operating conditions are collected systematically for variability study. Structural reliability is evaluated using Advanced First-Order Second-Moment Method (AFOSM). Finally, optimization formulation established and it has been discussed in this paper.

A Compromise Method for the Design of Parametric Polynomial Surrogate Models

2005 ◽  
Author(s):  
Pradeep George ◽  
Madara Ogot
Keyword(s):  
Response Surface ◽  
Multidisciplinary Design Optimization ◽  
Optimality Criterion ◽  
Surrogate Models ◽  
Function Evaluation ◽  
Design Quality ◽  
Response Surface Approximations ◽  
Polynomial Models ◽  
Polynomial Response Surface ◽  
Complex Mechanical Systems

This study presents a compromise approach to augmentation of response surface (RS) designs to achieve the desired level of accuracy. RS are frequently used as surrogate models in multidisciplinary design optimization of complex mechanical systems. Augmentation is necessitated by the high computational expense typically associated with each function evaluation. As a result previous results from lower fidelity models are incorporated into the higher fidelity RS designs. The compromise approach yields higher quality parametric polynomial response surface approximations than traditional augmentation. Based on the D-optimality criterion as a measure of RS design quality, the method simultaneously considers several polynomial models during the RS design, resulting in good quality designs for all models under consideration, as opposed to good quality designs only for lower order models as in the case of traditional augmentation. Several numerical and an engineering example are presented to illustrate the efficacy of the approach.

Evolutionary Multi-Objective Optimization Under Uncertainty Through Adaptive Kriging in Augmented Input Space

2019 ◽  
Vol 142 (1) ◽  
Author(s):  
J. Zhang ◽  
A. A. Taflanidis
Keyword(s):  
Uncertainty Quantification ◽  
Surrogate Model ◽  
Performance Comparison ◽  
Optimization Under Uncertainty ◽  
Optimization Scheme ◽  
Large Solution ◽  
Pareto Solution ◽  
Iterative Optimization ◽  
Multi Objective ◽  
Adaptive Kriging

Abstract This paper presents a surrogate model-based computationally efficient optimization scheme for design problems with multiple, probabilistic objectives estimated through stochastic simulation. It examines the extension of the previously developed MODU-AIM (Multi-Objective Design under Uncertainty with Augmented Input Metamodels) algorithm, which performs well for bi-objective problem but encounters scalability difficulties for applications with more than two objectives. Computational efficiency is achieved by using a single surrogate model, adaptively refined within an iterative optimization setting, to simultaneously support the uncertainty quantification and the design optimization, and the MODU-AIM extension is established by replacing the originally used epsilon-constraint optimizer with a multi-objective evolutionary algorithm (MOEA). This requires various modifications to accommodate MOEA’s unique traits. For uncertainty quantification, a clustering-based importance sampling density selection is introduced to mitigate MOEA’s lack of direct control on Pareto solution density. To address the potentially large solution set of MOEAs, both the termination criterion of the iterative optimization scheme and the design of experiment (DoE) strategy for refinement of the surrogate model are modified, leveraging efficient performance comparison indicators. The importance of each objective in the different parts of the Pareto front is further integrated in the DoE to improve the adaptive selection of experiments.

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