Homogeneous chaos basis adaptation for design optimization under uncertainty: Application to the oil well placement problem

2017 ◽  
Vol 31 (3) ◽  
pp. 265-276 ◽  
Author(s):  
Charanraj Thimmisetty ◽  
Panagiotis Tsilifis ◽  
Roger Ghanem
Keyword(s):  
Design Optimization ◽  
Adaptation Strategy ◽  
Optimization Under Uncertainty ◽  
Design Parameters ◽  
Oil Well ◽  
Placement Problem ◽  
Well Placement ◽  
One Dimensional ◽  
Optimization Framework ◽  
Low Dimensional

AbstractA new method is proposed for efficient optimization under uncertainty that addresses the curse of dimensionality as it pertains to the evaluation of probabilistic objectives and constraints. A basis adaptation strategy previously introduced by the authors is integrated into a design optimization framework that construes the optimization cost function as the quantity of interest and computes stochastic adapted bases as functions of design space parameters. With these adapted bases, the stochastic integrations at each design point are evaluated as low-dimensional integrals (mostly one dimensional). The proposed approach is demonstrated on a well-placement problem where the uncertainty is in the form of a stochastic process describing the permeability of the subsurface. An analysis of the method is carried out to better understand the effect of design parameters on the smoothness of the adaptation isometry.

Design-airworthiness integration method for general aviation aircraft during early development stage

2019 ◽  
Vol 91 (7) ◽  
pp. 1067-1076
Author(s):  
Maxim Tyan ◽  
Jungwon Yoon ◽  
Nhu Van Nguyen ◽  
Jae-Woo Lee ◽  
Sangho Kim
Keyword(s):  
Design Optimization ◽  
Design Stage ◽  
Design Parameters ◽  
Design Framework ◽  
Efficient Design ◽  
Content Type ◽  
Early Design ◽  
Early Design Stage ◽  
Optimization Framework ◽  
Design Requirements

Purpose Major changes of an aircraft configuration are conducted during the early design stage. It is important to include the airworthiness regulations at this stage while there is extensive freedom for designing. The purpose of this paper is to introduce an efficient design framework that integrates airworthiness guidelines and documentation at the early design stage. Design/methodology/approach A new design and optimization process is proposed that logically includes the airworthiness regulations as design parameters and constraints by constructing a certification database. The design framework comprises requirements analysis, preliminary sizing, conceptual design synthesis and loads analysis. A design certification relation table (DCRT) describes the legal regulations in terms of parameters and values suitable for use in design optimization. Findings The developed framework has been validated and demonstrated for the design of a Federal Aviation Regulations (FAR) 23 four-seater small aircraft. The validation results show an acceptable level of accuracy to be applied during the early design stage. The total mass minimization problem has been successfully solved while satisfying all the design requirements and certification constraints specified in the DCRT. Moreover, successful compliance with FAR 23 subpart C is demonstrated. The proposed method is a useful tool for design optimization and compliance verifications during the early stages of aircraft development. Practical implications The new certification database proposed in this research makes it simpler for engineers to access a large amount of legal documentation related to airworthiness regulations and provides a link between the regulation text and actual design parameters and their bounds. Originality/value The proposed design optimization framework integrates the certification database that is built of several types of legal documents such as regulations, advisory circulars and standards. The Engineering Requirements and Guide summarizes all the documents and design requirements into a single document. The DCRT is created as a summary table that indicates the design parameters affected by a given regulation(s), the design stage at which the parameter can be evaluated and its value bounds. The introduction of the certification database into the design optimization framework significantly reduces the engineer’s load related for airworthiness regulations.

scholarly journals Preliminary Sub-Systems Design Integrated in a Multidisciplinary Design Optimization Framework

2017 ◽  
Vol 2017 (4) ◽  
pp. 9-23
Author(s):  
Marco Fioriti ◽  
Luca Boggero ◽  
Sabrina Corpino
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Systems Design ◽  
Aircraft Design ◽  
Multidisciplinary Design ◽  
Design Parameters ◽  
Preliminary Design ◽  
Horizon 2020 ◽  
Optimization Framework ◽  
Complex Subject

Abstract The aircraft design is a complex subject since several and completely different design disciplines are involved in the project. Many efforts are made to harmonize and optimize the design trying to combine all disciplines together at the same level of detail. Within the ongoing AGILE (Horizon 2020) research, an aircraft MDO (Multidisciplinary Design Optimization) process is setting up connecting several design tools and competences together. Each tool covers a different design discipline such as aerodynamics, structure, propulsion and systems. This paper focuses on the integration of the sub-system design discipline with the others in order to obtain a complete and optimized aircraft preliminary design. All design parameters used to integrate the sub-system branch with the others are discussed as for their redefinition within the different detail level of the design.

scholarly journals Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Piyush Pandita ◽  
Ilias Bilionis ◽  
Jitesh Panchal
Keyword(s):  
Global Optimization ◽  
Stochastic Optimization ◽  
Objective Function ◽  
Information Acquisition ◽  
Optimization Problems ◽  
Epistemic Uncertainty ◽  
Optimization Under Uncertainty ◽  
Expected Improvement ◽  
Oil Well ◽  
Placement Problem

Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g., stochastic optimization or sampling average approximation, fail to learn exploitable patterns from collected data and require a lot of objective function evaluations. There is a need for techniques that alleviate the high cost of information acquisition and select sequential simulations optimally. In the field of deterministic single-objective unconstrained global optimization, the Bayesian global optimization (BGO) approach has been relatively successful in addressing the information acquisition problem. BGO builds a probabilistic surrogate of the expensive objective function and uses it to define an information acquisition function (IAF) that quantifies the merit of making new objective evaluations. In this work, we reformulate the expected improvement (EI) IAF to filter out parametric and measurement uncertainties. We bypass the curse of dimensionality, since the method does not require learning the response surface as a function of the stochastic parameters, and we employ a fully Bayesian interpretation of Gaussian processes (GPs) by constructing a particle approximation of the posterior of its hyperparameters using adaptive Markov chain Monte Carlo (MCMC) to increase the methods robustness. Also, our approach quantifies the epistemic uncertainty on the location of the optimum and the optimal value as induced by the limited number of objective evaluations used in obtaining it. We verify and validate our approach by solving two synthetic optimization problems under uncertainty and demonstrate it by solving the oil-well placement problem (OWPP) with uncertainties in the permeability field and the oil price time series.

On optimization algorithms for the reservoir oil well placement problem

2006 ◽  
Vol 10 (3) ◽  
pp. 303-319 ◽  
Author(s):  
W. Bangerth ◽  
H. Klie ◽  
M. F. Wheeler ◽  
P. L. Stoffa ◽  
M. K. Sen
Keyword(s):  
Optimization Algorithms ◽  
Oil Well ◽  
Placement Problem ◽  
Well Placement

Momentum exchange impact damper design methodology for object-wall-collision problems

2017 ◽  
Vol 24 (14) ◽  
pp. 3206-3218
Author(s):  
Yohei Kushida ◽  
Hiroaki Umehara ◽  
Susumu Hara ◽  
Keisuke Yamada
Keyword(s):  
Optimal Design ◽  
Design Methodology ◽  
Design Parameters ◽  
Momentum Exchange ◽  
Impact Damper ◽  
One Dimensional ◽  
Practical Design ◽  
Wall Collision ◽  
Damper Design ◽  
And Control

Momentum exchange impact dampers (MEIDs) were proposed to control the shock responses of mechanical structures. They were applied to reduce floor shock vibrations and control lunar/planetary exploration spacecraft landings. MEIDs are required to control an object’s velocity and displacement, especially for applications involving spacecraft landing. Previous studies verified numerous MEID performances through various types of simulations and experiments. However, previous studies discussing the optimal design methodology for MEIDs are limited. This study explicitly derived the optimal design parameters of MEIDs, which control the controlled object’s displacement and velocity to zero in one-dimensional motion. In addition, the study derived sub-optimal design parameters to control the controlled object’s velocity within a reasonable approximation to derive a practical design methodology for MEIDs. The derived sub-optimal design methodology could also be applied to MEIDs in two-dimensional motion. Furthermore, simulations conducted in the study verified the performances of MEIDs with optimal/sub-optimal design parameters.

Performance Modeling of Desiccant Wheels: 1 — Model Development

2008 ◽  
Author(s):  
Chaoqin Zhai ◽  
David H. Archer ◽  
John C. Fischer
Keyword(s):  
Model Development ◽  
Axial Direction ◽  
Performance Model ◽  
Design Parameters ◽  
Residual Water ◽  
One Dimensional ◽  
Operating Variables ◽  
Steady State Operation ◽  
Rotary Speed ◽  
The Impact

This paper presents the development of an equation based model to simulate the combined heat and mass transfer in the desiccant wheels. The performance model is one dimensional in the axial direction. It applies a lumped formulation in the thickness direction of the desiccant and the substrate. The boundary conditions of this problem represent the inlet outside/process and building exhaust/regeneration air conditions as well as the adiabatic condition of the two ends of the desiccant composite. The solutions of this model are iterated until the wheel reaches periodic steady state operation. The modeling results are obtained as the changes of the outside/process and building exhaust/regeneration air conditions along the wheel depth and the wheel rotation. This performance model relates the wheel’s design parameters, such as the wheel dimension, the channel size and the desiccant properties, and the wheel’s operating variables, such as the rotary speed and the regeneration air flowrate, to its operating performance. The impact of some practical issues, such as wheel purge, residual water in the desiccant and the wheel supporting structure, on the wheel performance has also been investigated.

Shape-design optimization of hull structures considering thermal deformation

2013 ◽  
Vol 228 (13) ◽  
pp. 2266-2277
Author(s):  
Myung-Jin Choi ◽  
Min-Geun Kim ◽  
Seonho Cho
Keyword(s):  
Optimal Design ◽  
Design Optimization ◽  
Thermal Deformation ◽  
Parametric Design ◽  
Optimization Method ◽  
Optimization Process ◽  
Design Parameters ◽  
Shape Design ◽  
Shape Design Optimization ◽  
Modified Method

We developed a shape-design optimization method for the thermo-elastoplasticity problems that are applicable to the welding or thermal deformation of hull structures. The point is to determine the shape-design parameters such that the deformed shape after welding fits very well to a desired design. The geometric parameters of curved surfaces are selected as the design parameters. The shell finite elements, forward finite difference sensitivity, modified method of feasible direction algorithm and a programming language ANSYS Parametric Design Language in the established code ANSYS are employed in the shape optimization. The objective function is the weighted summation of differences between the deformed and the target geometries. The proposed method is effective even though new design variables are added to the design space during the optimization process since the multiple steps of design optimization are used during the whole optimization process. To obtain the better optimal design, the weights are determined for the next design optimization, based on the previous optimal results. Numerical examples demonstrate that the localized severe deviations from the target design are effectively prevented in the optimal design.

Comparative Analysis of Methodologies for Uncertainty Propagation and Quantification

2017 ◽  
Author(s):  
Alessandra Cuneo ◽  
Alberto Traverso ◽  
Shahrokh Shahpar
Keyword(s):  
Engineering Design ◽  
Polynomial Chaos ◽  
Epistemic Uncertainty ◽  
Uncertainty Propagation ◽  
Computational Cost ◽  
Computational Effort ◽  
Optimization Under Uncertainty ◽  
Design Parameters ◽  
Test Case ◽  
Aleatory And Epistemic Uncertainty

In engineering design, uncertainty is inevitable and can cause a significant deviation in the performance of a system. Uncertainty in input parameters can be categorized into two groups: aleatory and epistemic uncertainty. The work presented here is focused on aleatory uncertainty, which can cause natural, unpredictable and uncontrollable variations in performance of the system under study. Such uncertainty can be quantified using statistical methods, but the main obstacle is often the computational cost, because the representative model is typically highly non-linear and complex. Therefore, it is necessary to have a robust tool that can perform the uncertainty propagation with as few evaluations as possible. In the last few years, different methodologies for uncertainty propagation and quantification have been proposed. The focus of this study is to evaluate four different methods to demonstrate strengths and weaknesses of each approach. The first method considered is Monte Carlo simulation, a sampling method that can give high accuracy but needs a relatively large computational effort. The second method is Polynomial Chaos, an approximated method where the probabilistic parameters of the response function are modelled with orthogonal polynomials. The third method considered is Mid-range Approximation Method. This approach is based on the assembly of multiple meta-models into one model to perform optimization under uncertainty. The fourth method is the application of the first two methods not directly to the model but to a response surface representing the model of the simulation, to decrease computational cost. All these methods have been applied to a set of analytical test functions and engineering test cases. Relevant aspects of the engineering design and analysis such as high number of stochastic variables and optimised design problem with and without stochastic design parameters were assessed. Polynomial Chaos emerges as the most promising methodology, and was then applied to a turbomachinery test case based on a thermal analysis of a high-pressure turbine disk.

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