Gaussian process surrogate model for an effective life assessment of transformer considering model and measurement uncertainties

2022 ◽  
Vol 134 ◽  
pp. 107401
Author(s):  
Syed Shadab ◽  
J. Hozefa ◽  
K. Sonam ◽  
Sushama Wagh ◽  
Navdeep M Singh
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Life Assessment ◽  
Measurement Uncertainties ◽  
Effective Life

scholarly journals A blackbox yield estimation workflow with Gaussian process regression applied to the design of electromagnetic devices

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Mona Fuhrländer ◽  
Sebastian Schöps
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Hybrid Approach ◽  
Gaussian Process Regression ◽  
Benchmark Problems ◽  
Yield Estimation ◽  
Electromagnetic Devices ◽  
Main Challenge ◽  
Sample Points ◽  
Stochastic Yield

Abstract In this paper an efficient and reliable method for stochastic yield estimation is presented. Since one main challenge of uncertainty quantification is the computational feasibility, we propose a hybrid approach where most of the Monte Carlo sample points are evaluated with a surrogate model, and only a few sample points are reevaluated with the original high fidelity model. Gaussian process regression is a non-intrusive method which is used to build the surrogate model. Without many prerequisites, this gives us not only an approximation of the function value, but also an error indicator that we can use to decide whether a sample point should be reevaluated or not. For two benchmark problems, a dielectrical waveguide and a lowpass filter, the proposed methods outperform classic approaches.

A Gaussian Process-Based Crack Pattern Modeling Approach for Battery Anode Materials Design

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Zhuoyuan Zheng ◽  
Bo Chen ◽  
Yanwen Xu ◽  
Nathan Fritz ◽  
Yashraj Gurumukhi ◽  
...  
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Lithium Ion ◽  
Building Blocks ◽  
Electrochemical Process ◽  
Fe Model ◽  
Model Framework ◽  
Major Drawback ◽  
Cracking Behavior ◽  
Si Anode

Abstract Silicon-based anodes are one of the promising candidates for the next generation high-power/energy density lithium ion batteries (LIBs). However, a major drawback limiting the practical application of the Si anode is that Si experiences a significant volume change during lithiation/delithiation, which induces high stresses causing degradation and pulverization of the anode. This study focuses on crack initiation within a Si anode during the delithiation process. A multi-physics-based finite element (FE) model is built to simulate the electrochemical process and crack generation during delithiation. On top of that, a Gaussian process (GP)-based surrogate model is developed to assist the exploration of the crack patterns within the anode design space. It is found that the thickness of the Si coating layer, TSi, the yield strength of the Si material, σFc, the cohesive strength between Si and the substrate, σFs, and the curvature of the substrate, ρ, have large impacts on the cracking behavior of Si. This coupled FE simulation-GP surrogate model framework is also applicable to other types of LIB electrodes and provides fundamental insights as building blocks to investigate more complex internal geometries.

A Rational Design Approach to Gaussian Process Modeling for Variable Fidelity Models

2011 ◽  
Author(s):  
Roxanne A. Moore ◽  
David A. Romero ◽  
Christiaan J. J. Paredis
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Rational Design ◽  
Sequential Sampling ◽  
Accurate Determination ◽  
Surrogate Modeling ◽  
Sampling Strategy ◽  
New Approach ◽  
Variable Fidelity ◽  
The Cost

Computer models and simulations are essential system design tools that allow for improved decision making and cost reductions during all phases of the design process. However, the most accurate models tend to be computationally expensive and can therefore only be used sporadically. Consequently, designers are often forced to choose between exploring many design alternatives with less accurate, inexpensive models and evaluating fewer alternatives with the most accurate models. To achieve both broad exploration of the design space and accurate determination of the best alternatives, surrogate modeling and variable accuracy modeling are gaining in popularity. A surrogate model is a mathematically tractable approximation of a more expensive model based on a limited sampling of that model. Variable accuracy modeling involves a collection of different models of the same system with different accuracies and computational costs. We hypothesize that designers can determine the best solutions more efficiently using surrogate and variable accuracy models. This hypothesis is based on the observation that very poor solutions can be eliminated inexpensively by using only less accurate models. The most accurate models are then reserved for discerning the best solution from the set of good solutions. In this paper, a new approach for global optimization is introduced, which uses variable accuracy models in conjuction with a kriging surrogate model and a sequential sampling strategy based on a Value of Information (VOI) metric. There are two main contributions. The first is a novel surrogate modeling method that accommodates data from any number of different models of varying accuracy and cost. The proposed surrogate model is Gaussian process-based, much like classic kriging modeling approaches. However, in this new approach, the error between the model output and the unknown truth (the real world process) is explicitly accounted for. When variable accuracy data is used, the resulting response surface does not interpolate the data points but provides an approximate fit giving the most weight to the most accurate data. The second contribution is a new method for sequential sampling. Information from the current surrogate model is combined with the underlying variable accuracy models’ cost and accuracy to determine where best to sample next using the VOI metric. This metric is used to mathematically determine where next to sample and with which model. In this manner, the cost of further analysis is explicitly taken into account during the optimization process.

Antenna Resonant Frequency Modeling based on AdaBoost Gaussian Process Ensemble

2021 ◽  
Vol 35 (12) ◽  
pp. 1485-1492
Author(s):  
Tianliang Zhang ◽  
Yubo Tian ◽  
Xuezhi Chen ◽  
Jing Gao
Keyword(s):  
Resonant Frequency ◽  
Gaussian Process ◽  
Surrogate Model ◽  
Coplanar Waveguide ◽  
Optimization Methods ◽  
Adaboost Algorithm ◽  
Full Wave ◽  
Modeling Experiment ◽  
Global Optimization Methods ◽  
Rectangular Microstrip Antenna

The design of electromagnetic components generally relies on simulation of full-wave electromagnetic field software exploiting global optimization methods. The main problem of the method is time consuming. Aiming at solving the problem, this study proposes a regression surrogate model based on AdaBoost Gaussian process (GP) ensemble (AGPE). In this method, the GP is used as the weak model, and the AdaBoost algorithm is introduced as the ensemble framework to integrate the weak models, and the strong learner will eventually be used as a surrogate model. Numerical simulation experiment is used to verify the effectiveness of the model, the mean relative error (MRE) of the three classical benchmark functions decreases, respectively, from 0.0585, 0.0528, 0.0241 to 0.0143, 0.0265, 0.0116, and then the method is used to model the resonance frequency of rectangular microstrip antenna (MSA) and coplanar waveguide butterfly MSA. The MRE of test samples based on the APGE are 0.0069, 0.0008 respectively, and the MRE of a single GP are 0.0191, 0.0023 respectively. The results show that, compared with a single GP regression model, the proposed AGPE method works better. In addition, in the modeling experiment of resonant frequency of rectangular MSA, the results obtained by AGPE are compared with those obtained by using neural network (NN). The results show that the proposed method is more effective.

Aerodynamic optimization to tandem stators by using sweep and dihedral

2020 ◽  
Vol 234 (6) ◽  
pp. 1225-1236
Author(s):  
Jinouwen Zhang ◽  
Haowan Zhuang ◽  
Jinfang Teng ◽  
Mingmin Zhu ◽  
Xiaoqing Qiang
Keyword(s):  
Gaussian Process ◽  
Surrogate Model ◽  
Aerodynamic Performance ◽  
High Dimensional ◽  
Two Dimensional ◽  
Multi Objective Optimization ◽  
Optimization Scheme ◽  
Multi Objective ◽  
Design Variables ◽  
Dimensional Optimization

In the modern aerodynamic design of turbomachinery blades, the geometries of blades often need to be reshaped to achieve better aerodynamic performance by introducing extra parametric design variables. A higher variable dimension will lead to a larger sampling range as well as a sparser sample distribution, which challenges the effectiveness and stability of optimization schemes based on surrogate model by making the model prediction quality even poorer. In this paper, a multi-objective optimization based on Gaussian process model was carried out for a high dimensional design space. Based on the previous two-dimensional optimization, tandem stators of a modern compressor were optimized by the design of sweep and dihedral. The purpose of the study is to improve the aerodynamic performance of the compressor tandem stators as well as to provide an effective optimization scheme for high dimensional multi-objective optimization problems. The design of sweep and dihedral for reshaping the tandem stators consists of a total of 18 design variables. An improvement in total pressure recovery coefficient of at least 0.7% at positive incidence and at least 0.3% at negative incidence was obtained, much larger than that in the previous two-dimensional optimization. The optimization process shows that, by using Gaussian process as the surrogate model and a special sampling strategy, this optimization scheme is effective and efficient to handle this high dimensional space. The aerodynamic influences of design parameters of tandem blades were analyzed in detail and the superiority of sweep and dihedral in reducing aerodynamic loss was confirmed.

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