Multi-objective design and optimization of a vienna rectifier with parametric uncertainty quantification

2017 ◽  
Author(s):  
Niloofar Rashidi Mehrabadi ◽  
Qiong Wang ◽  
Rolando Burgos ◽  
Dushan Boroyevich
Keyword(s):  
Uncertainty Quantification ◽  
Parametric Uncertainty ◽  
Design And Optimization ◽  
Multi Objective ◽  
Vienna Rectifier

Interactive Multi‐objective Dynamic Optimization of Bioreactors under Parametric Uncertainty

2018 ◽  
Author(s):  
Philippe Nimmegeers ◽  
Mattia Vallerio ◽  
Dries Telen ◽  
Jan Impe ◽  
Filip Logist
Keyword(s):  
Dynamic Optimization ◽  
Parametric Uncertainty ◽  
Multi Objective

scholarly journals RANGE EXTENDER ICE MULTI-PARAMETRIC MULTI-OBJECTIVE OPTIMIZATION

2021 ◽  
Vol 18 (1) ◽  
pp. 10
Author(s):  
Mikuláš Adámek ◽  
Rastislav Toman
Keyword(s):  
Simulation Model ◽  
Combustion Engine ◽  
Raw Materials ◽  
Single Point ◽  
Fuel Efficiency ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Electric Generator ◽  
Design And Optimization ◽  
Multi Objective

Range Extended Electric Vehicles (REEV) are still one of the suitable concepts for modern sustainable low emission vehicles. REEV is equipped with a small and lightweight unit, comprised usually of an internal combustion engine with an electric generator, and has thus the technical potential to overcome the main limitations of a pure electric vehicle – range anxiety, overall driving range, heating, and air-conditioning demands – using smaller battery: saving money, and raw materials. Even though several REx ICE concepts were designed in past, most of the available studies lack more complex design and optimization approach, not exploiting the advantageous single point operation of these engines. Resulting engine designs are usually rather conservative, not optimized for the best efficiency. This paper presents a multi-parametric and multi-objective optimization approach, that is applied on a REx ICE. Our optimization toolchain combines a parametric GT-Suite ICE simulation model, modeFRONTIER optimization software with various optimization strategies, and a parametric CAD model, that first provides some simulation model inputs, and second also serves for the final designs’ feasibility check. The chosen ICE concept is a 90 degrees V-twin engine, four-stroke, spark-ignition, naturally aspirated, port injected, OHV engine. The optimization goal is to find the thermodynamic optima for three different design scenarios of our concept – three different engine displacements – addressing the compactness requirement of a REx ICE. The optimization results show great fuel efficiency potential by applying our optimization methodology, following the general trends in increasing ICE efficiency, and power for a naturally aspirated concept.

Research on Design and Optimization on Balance System of Large Floating Crane

2013 ◽  
Vol 744 ◽  
pp. 174-179
Author(s):  
Hai Rong Shen ◽  
Yong Sheng Yang
Keyword(s):  
Mathematical Model ◽  
Optimization Design ◽  
Metal Structure ◽  
Optimal Result ◽  
Main Research ◽  
Design And Optimization ◽  
Multi Objective ◽  
Balance System ◽  
Overturning Stability ◽  
Floating Crane

In this paper, the optimization of floating crane balance system metal structure is main research purpose. So, firstly, optimization design mathematical model of floating crane balance system is constructed concretely under the multi-objective circumstance. Secondly, the optimization results are obtained based on genetic algorithmic, the optimal result are compared to the non-optimized value and the value reduces 16.74%. Finally, according to the resistive overturning stability requirement of floating crane, the optimization results are to be stability checked, so the rationality of optimization results are to be proved.

Parametric uncertainty quantification in the Rothermel model with randomised quasi-Monte Carlo methods

2015 ◽  
Vol 24 (3) ◽  
pp. 307 ◽  
Author(s):  
Yaning Liu ◽  
Edwin Jimenez ◽  
M. Yousuff Hussaini ◽  
Giray Ökten ◽  
Scott Goodrick
Keyword(s):  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Monte Carlo Methods ◽  
Wildland Fire ◽  
Parametric Uncertainty ◽  
Monte Carlo Sampling ◽  
Model Parameters ◽  
Quasi Monte Carlo ◽  
Control Variate ◽  
Fuel Model

Rothermel's wildland surface fire model is a popular model used in wildland fire management. The original model has a large number of parameters, making uncertainty quantification challenging. In this paper, we use variance-based global sensitivity analysis to reduce the number of model parameters, and apply randomised quasi-Monte Carlo methods to quantify parametric uncertainties for the reduced model. The Monte Carlo estimator used in these calculations is based on a control variate approach applied to the sensitivity derivative enhanced sampling. The chaparral fuel model, selected from Rothermel's 11 original fuel models, is studied as an example. We obtain numerical results that improve the crude Monte Carlo sampling by factors as high as three orders of magnitude.

scholarly journals Using an Uncertainty Quantification Framework to Calibrate the Runoff Generation Scheme in E3SM Land Model V1

2021 ◽  
Author(s):  
Donghui Xu ◽  
Gautam Bisht ◽  
Khachik Sargsyan ◽  
Chang Liao ◽  
L. Ruby Leung
Keyword(s):  
Uncertainty Quantification ◽  
Water Cycle ◽  
Computational Cost ◽  
Model Performance ◽  
Parametric Uncertainty ◽  
Surrogate Models ◽  
Earth System ◽  
Runoff Generation ◽  
Parameter Values ◽  
Sensitive Parameters

Abstract. Runoff is a critical component of the terrestrial water cycle and Earth System Models (ESMs) are essential tools to study its spatio-temporal variability. Runoff schemes in ESMs typically include many parameters so model calibration is necessary to improve the accuracy of simulated runoff. However, runoff calibration at global scale is challenging because of the high computational cost and the lack of reliable observational datasets. In this study, we calibrated 11 runoff relevant parameters in the Energy Exascale Earth System Model (E3SM) Land Model (ELM) using an uncertainty quantification framework. First, the Polynomial Chaos Expansion machinery with Bayesian Compressed Sensing is used to construct computationally inexpensive surrogate models for ELM-simulated runoff at 0.5° × 0.5° for 1991–2010. The main methodological advance in this work is the construction of surrogates for the error metric between ELM and the benchmark data, facilitating efficient calibration and avoiding the more conventional, but challenging, construction of high-dimensional surrogates for ELM itself. Second, the Sobol index sensitivity analysis is performed using the surrogate models to identify the most sensitive parameters, and our results show that in most regions ELM-simulated runoff is strongly sensitive to 3 of the 11 uncertain parameters. Third, a Bayesian method is used to infer the optimal values of the most sensitive parameters using an observation-based global runoff dataset as the benchmark. Our results show that model performance is significantly improved with the inferred parameter values. Although the parametric uncertainty of simulated runoff is reduced after the parameter inference, it remains comparable to the multi-model ensemble uncertainty represented by the global hydrological models in ISMIP2a. Additionally, the annual global runoff trend during the simulation period is not well constrained by the inferred parameter values, suggesting the importance of including parametric uncertainty in future runoff projections.

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