No full-text available
To read the full-text of this research,
you can request a copy directly from the authors.
A Novel Robust Design Method Based on the Coordinated Mechanism of Surrogate Models
ResearchGate has not been able to resolve any citations for this publication.
In the face of the pressing environmental issues, the past decade witnessed the booming development of the distributed energy systems (DESs). A notable problem of DESs is the inevitable uncertainty that may make DESs deviate significantly from the deterministically obtained expectations, in both aspects of optimal design and economic operation. It thus necessitates the sensitivity analysis to quantify the impacts of the massive parametric uncertainties. This paper aims to give a comprehensive quantification, and carries out a multi-stage sensitivity analysis on DESs from the perspectives of evaluation criteria, optimal design and economic operation. First, a mathematical model of a DES is developed to present the solutions to the three stages of the DES. Second, the Monte-Carlo simulation is carried out subject to the probabilistic distributions of the energy, technical and economic parameters. Based on the simulation results, the variance-based Sobol method is applied to calculate the individual importance, interactional importance and total importance of various parameters. The comparison of the multi-stage results shows that only a few parameters play critical roles while the uncertainty of most of the massive parameters has little impact on the system performance. In addition, the influence of parameter interactions in the optimal design stage are much stronger than that in the evaluation criteria and operation strategy stages.
Efficient experiment design is of great significance for the validation of simulation model with high nonlinearity and large input space. Excessive validation experiment raises the cost while insufficient test increases the risks of accepting an invalid model. In this paper, an adaptive sequential experiment design method combining global exploration criterion and local exploitation criterion is proposed. The exploration criterion utilizes discrepancy metric to improve the space-filling property of the design points while the exploitation criterion employs the leave one out error to discover informative points. To avoid the clustering of samples in the local region, an adaptive weight updating approach is provided to maintain the balance between exploration and exploitation. Besides, the credibility distribution function characterizing the relationship between the input and result credibility is introduced to support the model validation experiment design. Finally, six benchmark problems and an engineering case are applied to examine the performance of the proposed method. The experiments indicate that the proposed method achieves satisfactory performance for function approximation in accuracy and convergence.
Integrated parameter and tolerance design is a cost‐effective method to multiresponse quality improvement. However, previous methods usually ignore model parameter uncertainty, dispersion effect, or correlation among responses. This may lead to the obtained optimal solutions far from the true optimal values of parameters and tolerances. To address the problem, a novel integrated parameter and tolerance design method is proposed to solve correlated multiple response problems under consideration of model parameter uncertainty, the location and dispersion effects of the quality loss, and the tolerance costs simultaneously. As there usually exists uncertainty in the quality loss and tolerance costs, multiobjective optimization is adopted to seek for the robust optimal solutions. The effectiveness and robustness of the proposed method are illustrated with a practical example and a random simulation example. The results show that the proposed method provides more reasonable results in quality improvement and cost reduction than those of the existing methods.
The use of surrogate models (response surface models, curve fits) of various types (radial basis functions, Gaussian process models, neural networks, support vector machines, etc.) is now an accepted way for speeding up design search and optimization in many fields of engineering that require the use of expensive computer simulations, including problems with multiple goals and multiple domains. Surrogates are also widely used in dealing with uncertainty quantification of expensive black-box codes where there are strict limits on the number of function evaluations that can be afforded in estimating the statistical properties of derived performance quantities. Here, we tackle the problem of robust design optimization from the direction of Gaussian process models (Kriging). We contrast two previously studied models, co-Kriging and combined Kriging (sometimes called level 1 Kriging), and propose a new combined approach called combined co-Kriging that attempts to make best use of the key ideas present in these methods.
Lightweight design is important for the Thermal Protection System (TPS) of hypersonic vehicles in that it protects the inner structure from severe heating environment. However, due to the existence of uncertainties in material properties and geometry, it is imperative to incorporate uncertainty analysis into the design optimization to obtain reliable results. In this paper, a six sigma robust design optimization based on Successive Response Surface Method (SRSM) is established for the TPS to improve the reliability and robustness with considering the uncertainties. The uncertain parameters related to material properties and thicknesses of insulation layers are considered and characterized by random variables following normal distributions. By employing SRSM, the values of objective function and constraints are approximated by the response surfaces to reduce computational cost. The optimization is an iterative process with response surfaces updating to find the true optimal solution. The optimization of the nose cone of hypersonic vehicle cabin is provided as an example to illustrate the feasibility and effectiveness of the proposed method. Keywords: Hypersonic vehicle, Six sigma robust optimization, Successive response surface, Thermal protection system, Uncertainty
In many engineering optimization problems, the number of function evaluations is severely limited by time or cost. These problems pose a special challenge to the field of global optimization, since existing methods often require more function evaluations than can be comfortably afforded. One way to address this challenge is to fit response surfaces to data collected by evaluating the objective and constraint functions at a few points. These surfaces can then be used for visualization, tradeoff analysis, and optimization. In this paper, we introduce the reader to a response surface methodology that is especially good at modeling the nonlinear, multimodal functions that often occur in engineering. We then show how these approximating functions can be used to construct an efficient global optimization algorithm with a credible stopping rule. The key to using response surfaces for global optimization lies in balancing the need to exploit the approximating surface (by sampling where it is minimized) with the need to improve the approximation (by sampling where prediction error may be high). Striking this balance requires solving certain auxiliary problems which have previously been considered intractable, but we show how these computational obstacles can be overcome.
This paper presents a taxonomy of existing approaches for using response surfaces for global optimization. Each method is illustrated with a simple numerical example that brings out its advantages and disadvantages. The central theme is that methods that seem quite reasonable often have non-obvious failure modes. Understanding these failure modes is essential for the development of practical algorithms that fulfill the intuitive promise of the response surface approach. Key words: global optimization, response surface, kriging, splines 1.
Objective
As healthcare costs continue to rise, healthcare consumers expect service quality to improve. The aim of this study was to investigate the influence of Total Quality Management by a healthcare facility on perceived service quality and patient satisfaction.
Method
A self-completion questionnaire was submitted to inpatients who were treated in April 2018 at a tertiary-level hospital in Vietnam. There were 516 respondents. The Statistical Package for the Social Sciences (SPSS) ver. 25.0 and Amos ver. 25.0 programmes were used for frequency and descriptive statistical analysis, and structural equation modelling, respectively.
Results
Total Quality Management had a significant effect on perceived service quality and patient satisfaction; perceived service quality had a positive influence on patient satisfaction.
Conclusion
We used Total Quality Management and perceived service quality to develop a model showing that both factors directly influenced patient satisfaction. The work has significant implications for healthcare organizations; they may wish to consider these factors when engaging in strategic planning aimed at improving customer satisfaction. Those responsible for ensuring service quality should consider these factors.
Over the last few years, the importance of non-deterministic techniques in Geotechnical engineering is highlighted in literature. Random set (RS) theory is recently being utilized for reliability analysis of geotechnical problems, however, its application in deep urban excavation is not yet investigated for a real case study. Hence, in this paper, an attempt is made to investigate the feasibility of the RS method in determining the reliability of deep excavations. For this reason, a 27-meter-deep urban excavation in Tehran, Iran is considered as a case study. The excavation is numerically modelled. Subsequently the reliability analysis is performed. Findings indicate that the probability of collapse of excavation is as low as 10−7 which suggests that the project is safe against collapse. The aforementioned conclusion is confirmed when RS results were compared with site observation. Additionally, RS results were evaluated using Monte Carlo (MC) technique. Although further studies for more real case studies are recommended, good agreement between the results of MC and RS methods suggests the feasibility of RS technique for estimating the reliability of deep urban excavations
In this article, we present a detailed overview of the literature on the design of computer experiments. We classify the existing literature broadly into two categories, viz. static and adaptive design of experiments (DoE). We begin with the abundant literature available on static DoE, its chronological evolution, and its pros and cons. Our discussion naturally points to the challenges that are faced by the static techniques. The adaptive DoE techniques employ intelligent and iterative strategies to address these challenges by combining system knowledge with space-filling for sample placement. We critically analyze the adaptive DoE literature based on the key features of placement strategies. Our numerical and visual analyses of the static DoE techniques reveal the excellent performance of Sobol sampling (SOB3) for higher dimensions; and that of Hammersley (HAM) and Halton (HAL) sampling for lower dimensions. Finally, we provide several potential opportunities for the future modern DoE research.
Surrogate models are widely used to predict response function of any system and in quantifying uncertainty associated with the response function. It is required to have response quantities at some preselected sample points to construct a surrogate model which can be processed in two way. Either the surrogate model is constructed using one shot experimental design techniques, or, the sample points can be generated in a sequential manner so that optimum sample points for a specific problem can be obtained. This paper addresses a comprehensive comparisons between these two types of sampling techniques for the construction of more accurate surrogate models. Two most popular one shot sampling strategies: Latin hypercube sampling and Sobol sequence, and four different type sequential experimental designs (SED) namely, Monte Carlo intersite projected (MCIP), Monte Carlo intersite projected threshold (MCIPT), optimizer projected (OP) and LOLA-Voronoi (LV) method are taken for the present study. Two most widely used surrogate models, namely polynomial chaos expansion and Kriging are used to check the applicability of the experimental design techniques. Three different types of numerical problems are solved using the two above-mentioned surrogate models considering all the experimental design techniques independently. Further, all the results are compared with the standard Monte Carlo simulation (MCS). Overall study depicts that SED performs well in predicting the response functions more accurately with an acceptable number of sample points even for high-dimensional problems which maintains the balance between accuracy and efficiency. More specifically, MCIPT and LV method based Kriging outperforms other combinations.
A system-level design optimization method under the framework of a deterministic approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the deterministic approach. Therefore, a robust approach based on the technique of design for six sigma is presented for the system-level design optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust approach.
The dual response surface methodology is one of the most commonly used approaches in robust parameter design to simultaneously optimize the mean value and keep the variance minimum. The commonly used meta-model is the quadratic polynomial regression. For highly nonlinear input/output relationship, the accuracy of the fitted model is limited. Many researchers recommended to use more complicated surrogate models. In this study, three surrogate models will replace the second order polynomial regression, namely, ordinary Kriging, radial basis function approximation (RBF) and radial basis function artificial neural network (RBFNN). The results show that the three surrogate model present superior accuracy in comparison with the quadratic polynomial regression.
The mean squared error (MSE) approach is widely used to link the mean and variance in one cost function. In this study, a new approach has been proposed using multi-objective optimization. The new approach has two main advantages over the classical method. First, the conflicting nature of the two objectives can be efficiently handled. Second, the decision maker will have a set of Pareto-front design points to select from.
Evaluating random set technique for reliability analysis of deep urban excavation using monte carlo simulation[J]
- E Momeni
- M Poormoosavian
- A Mahdiyar