A New Variable-Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling

2008 ◽  
Vol 130 (11) ◽  
Author(s):  
Ying Xiong ◽  
Wei Chen ◽  
Kwok-Leung Tsui
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Surrogate Models ◽  
High Fidelity ◽  
Design Decision ◽  
Model Fusion ◽  
Design Objective ◽  
Optimization Framework ◽  
Variable Fidelity ◽  
High Fidelity Simulations

Computational models with variable fidelity have been widely used in engineering design. To alleviate the computational burden, surrogate models are used for optimization without directly invoking expensive high-fidelity simulations. In this work, a model fusion technique based on the Bayesian–Gaussian process modeling is employed to construct cheap surrogate models to integrate information from both low-fidelity and high-fidelity models, while the interpolation uncertainty of the surrogate model due to the lack of sufficient high-fidelity simulations is quantified. In contrast to space filling, the sequential sampling of a high-fidelity simulation model in our proposed framework is objective-oriented, aiming for improving a design objective. Strategy based on periodical switching criteria is studied, which is shown to be effective in guiding the sequential sampling of a high-fidelity model toward improving a design objective as well as reducing the interpolation uncertainty. A design confidence metric is proposed as the stopping criterion to facilitate design decision making against the interpolation uncertainty. Examples are provided to illustrate the key ideas and features of model fusion, sequential sampling, and design confidence—the three key elements in the proposed variable-fidelity optimization framework.

A New Variable Fidelity Optimization Framework Based on Model Fusion and Objective-Oriented Sequential Sampling

2007 ◽  
Author(s):  
Ying Xiong ◽  
Wei Chen ◽  
Kwok-Leung Tsui
Keyword(s):  
Computational Models ◽  
Sequential Sampling ◽  
Surrogate Models ◽  
High Fidelity ◽  
Design Decision ◽  
Model Fusion ◽  
Design Objective ◽  
Variable Fidelity ◽  
High Fidelity Simulations ◽  
Fidelity Model

Computational models with variable fidelity have been widely used in engineering design. To alleviate the computational burden, surrogate models are used for optimization without recourse to expensive high-fidelity simulations. In this work, a model fusion technique based on Bayesian Gaussian process modeling is employed to construct cheap, surrogate models to integrate information from both low-fidelity and high-fidelity models, while the interpolation uncertainty of the surrogate model due to the lack of sufficient high-fidelity simulations is quantified. In contrast to space filling, the sequential sampling of a high-fidelity simulation model in our proposed framework is objective-oriented, aiming for improving a design objective. Strategy based on periodical switching criteria is studied which is shown to be effective in guiding the sequential sampling of a high-fidelity model towards improving a design objective as well as reducing the interpolation uncertainty. A design confidence (DC) metric is proposed to serves as the stopping criterion to facilitate design decision making against the interpolation uncertainty. Numerical and engineering examples are provided to demonstrate the benefits of the proposed methodology.

scholarly journals Multi-Fidelity Optimization of a Composite Airliner Wing Subject to Structural and Aeroelastic Constraints

Aerospace ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 398
Author(s):  
Angelos Kafkas ◽  
Spyridon Kilimtzidis ◽  
Athanasios Kotzakolios ◽  
Vassilis Kostopoulos ◽  
George Lampeas
Keyword(s):  
Low Cost ◽  
Computational Cost ◽  
Design Stage ◽  
Computational Time ◽  
High Fidelity ◽  
Full Potential ◽  
Optimized Design ◽  
Structural Representation ◽  
Optimization Framework ◽  
Variable Fidelity

Efficient optimization is a prerequisite to realize the full potential of an aeronautical structure. The success of an optimization framework is predominately influenced by the ability to capture all relevant physics. Furthermore, high computational efficiency allows a greater number of runs during the design optimization process to support decision-making. The efficiency can be improved by the selection of highly optimized algorithms and by reducing the dimensionality of the optimization problem by formulating it using a finite number of significant parameters. A plethora of variable-fidelity tools, dictated by each design stage, are commonly used, ranging from costly high-fidelity to low-cost, low-fidelity methods. Unfortunately, despite rapid solution times, an optimization framework utilizing low-fidelity tools does not necessarily capture the physical problem accurately. At the same time, high-fidelity solution methods incur a very high computational cost. Aiming to bridge the gap and combine the best of both worlds, a multi-fidelity optimization framework was constructed in this research paper. In our approach, the low-fidelity modules and especially the equivalent-plate methodology structural representation, capable of drastically reducing the associated computational time, form the backbone of the optimization framework and a MIDACO optimizer is tasked with providing an initial optimized design. The higher fidelity modules are then employed to explore possible further gains in performance. The developed framework was applied to a benchmark airliner wing. As demonstrated, reasonable mass reduction was obtained for a current state of the art configuration.

High-Fidelity Models and Multiobjective Global Optimization Algorithms in Simulation-Based Design

2005 ◽  
Vol 49 (03) ◽  
pp. 159-175
Author(s):  
Daniele Peri ◽  
Emilio F. Campana
Keyword(s):  
Global Optimization ◽  
Optimization Technique ◽  
Surrogate Models ◽  
Optimization Process ◽  
High Fidelity ◽  
Objective Functions ◽  
Analytical Approximations ◽  
Simulation Based Design ◽  
Simulation Based ◽  
Variable Fidelity

This work presents a simulation-based design environment for the solution of optimum ship design problems based on a global optimization (GO) algorithm that prevents the optimizer from being trapped into local minima. The procedure, illustrated in the framework of multiobjective optimization problems, makes use of high-fidelity, CPU-time-expensive computational models, including a free surface-capturing Reynolds-averaged Navier Stokes equation (RANSE) solver. The optimization process is composed of a global and a local phase. In the global stage of the search, a few computationally expensive simulations are needed for creating analytical approximations(i.e., surrogate models) of the objective functions. Tentative designs, created to explore the design space, are then evaluated with these inexpensive approximations. The more promising designs are then clustered and locally minimized and eventually verified with high-fidelity simulations. New exact values are used to improve the surrogate models, and repeated cycles of the algorithm are performed. A decision maker strategy is finally adopted to select the more interesting solution, and a final local refinement stage is performed by a gradient-based local optimization technique. A key point in the algorithm is the introduction of the surrogate models for the reduction of the overall time needed for the objective functions evaluation and their dynamic evolution and refinement along the optimization process. Moreover, an attractive alternative to adjoint formulations, the approximation management framework (AMF), based on a combined strategy that joins variable fidelity models and trust region techniques, is tested. Numerical examples are given demonstrating both the validity and usefulness of the proposed approach.

scholarly journals Peering inside a cough or sneeze to explain enhanced airborne transmission under dry weather

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Liu ◽  
Majid Allahyari ◽  
Jorge S. Salinas ◽  
Nadim Zgheib ◽  
S. Balachandar
Keyword(s):  
High Fidelity ◽  
Substantial Variation ◽  
Ambient Conditions ◽  
Airborne Transmission ◽  
Social Distancing ◽  
Virtual Experiments ◽  
Long Time ◽  
Simulation Results ◽  
High Fidelity Simulations ◽  
Droplet Nuclei

AbstractHigh-fidelity simulations of coughs and sneezes that serve as virtual experiments are presented, and they offer an unprecedented opportunity to peer into the chaotic evolution of the resulting airborne droplet clouds. While larger droplets quickly fall-out of the cloud, smaller droplets evaporate rapidly. The non-volatiles remain airborne as droplet nuclei for a long time to be transported over long distances. The substantial variation observed between the different realizations has important social distancing implications, since probabilistic outlier-events do occur and may need to be taken into account when assessing the risk of contagion. Contrary to common expectations, we observe dry ambient conditions to increase by more than four times the number of airborne potentially virus-laden nuclei, as a result of reduced droplet fall-out through rapid evaporation. The simulation results are used to validate and calibrate a comprehensive multiphase theory, which is then used to predict the spread of airborne nuclei under a wide variety of ambient conditions.

scholarly journals Model-guided design of mammalian genetic programs

2021 ◽  
Vol 7 (8) ◽  
pp. eabe9375
Author(s):  
J. J. Muldoon ◽  
V. Kandula ◽  
M. Hong ◽  
P. S. Donahue ◽  
J. D. Boucher ◽  
...  
Keyword(s):  
Mammalian Cells ◽  
Computational Models ◽  
Biological Complexity ◽  
Genetic Circuits ◽  
Cellular Functions ◽  
High Performing ◽  
Design Objective ◽  
Complex Functions ◽  
Mammalian Genetic ◽  
Analog Processing

Genetically engineering cells to perform customizable functions is an emerging frontier with numerous technological and translational applications. However, it remains challenging to systematically engineer mammalian cells to execute complex functions. To address this need, we developed a method enabling accurate genetic program design using high-performing genetic parts and predictive computational models. We built multifunctional proteins integrating both transcriptional and posttranslational control, validated models for describing these mechanisms, implemented digital and analog processing, and effectively linked genetic circuits with sensors for multi-input evaluations. The functional modularity and compositional versatility of these parts enable one to satisfy a given design objective via multiple synonymous programs. Our approach empowers bioengineers to predictively design mammalian cellular functions that perform as expected even at high levels of biological complexity.

A two-stage design optimization framework for multifield origami-inspired structures

2021 ◽  
pp. 1045389X2110116
Author(s):  
Wei Zhang ◽  
Saad Ahmed ◽  
Jonathan Hong ◽  
Zoubeida Ounaies ◽  
Mary Frecker
Keyword(s):  
Case Studies ◽  
Computing Time ◽  
Computational Cost ◽  
High Fidelity ◽  
Fe Model ◽  
Two Stage ◽  
Baseline Design ◽  
Optimization Framework ◽  
Highly Nonlinear ◽  
Stage 1

Different types of active materials have been used to actuate origami-inspired self-folding structures. To model the highly nonlinear deformation and material responses, as well as the coupled field equations and boundary conditions of such structures, high-fidelity models such as finite element (FE) models are needed but usually computationally expensive, which makes optimization intractable. In this paper, a computationally efficient two-stage optimization framework is developed as a systematic method for the multi-objective designs of such multifield self-folding structures where the deformations are concentrated in crease-like areas, active and passive materials are assumed to behave linearly, and low- and high-fidelity models of the structures can be developed. In Stage 1, low-fidelity models are used to determine the topology of the structure. At the end of Stage 1, a distance measure [Formula: see text] is applied as the metric to determine the best design, which then serves as the baseline design in Stage 2. In Stage 2, designs are further optimized from the baseline design with greatly reduced computing time compared to a full FEA-based topology optimization. The design framework is first described in a general formulation. To demonstrate its efficacy, this framework is implemented in two case studies, namely, a three-finger soft gripper actuated using a PVDF-based terpolymer, and a 3D multifield example actuated using both the terpolymer and a magneto-active elastomer, where the key steps are elaborated in detail, including the variable filter, metrics to select the best design, determination of design domains, and material conversion methods from low- to high-fidelity models. In this paper, analytical models and rigid body dynamic models are developed as the low-fidelity models for the terpolymer- and MAE-based actuations, respectively, and the FE model of the MAE-based actuation is generalized from previous work. Additional generalizable techniques to further reduce the computational cost are elaborated. As a result, designs with better overall performance than the baseline design were achieved at the end of Stage 2 with computing times of 15 days for the gripper and 9 days for the multifield example, which would rather be over 3 and 2 months for full FEA-based optimizations, respectively. Tradeoffs between the competing design objectives were achieved. In both case studies, the efficacy and computational efficiency of the two-stage optimization framework are successfully demonstrated.

High-fidelity simulations of moving and flexible airfoils at low Reynolds numbers

2009 ◽  
Vol 46 (5) ◽  
pp. 903-922 ◽  
Author(s):  
Miguel R. Visbal ◽  
Raymond E. Gordnier ◽  
Marshall C. Galbraith
Keyword(s):  
Reynolds Numbers ◽  
High Fidelity ◽  
Low Reynolds Numbers ◽  
Low Reynolds ◽  
High Fidelity Simulations ◽  
Flexible Airfoils
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