scholarly journals Two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model

2021 ◽  
Vol 39 (1) ◽  
pp. 148-158
Author(s):  
Ji Miao ◽  
Chunlin Gong ◽  
Chunna Li
Keyword(s):  
Optimization Method ◽  
Aerodynamic Performance ◽  
Early Termination ◽  
Computational Time ◽  
High Fidelity ◽  
Aerodynamic Optimization ◽  
Two Stage ◽  
Optimization Stage ◽  
Variable Fidelity ◽  
Fidelity Model

Efficient aerodynamic design optimization method is of great value for improving the aerodynamic performance of little UAV's airfoil. Using engineering or semi-engineering estimation method to analyze aerodynamic forces in solving aerodynamic optimization problems costs little computational time, but the accuracy cannot be guaranteed. However, CFD method ensuring high accuracy needs much more computational cost, which is unfordable for optimization. Surrogate-based optimization can reduce the number of high-fidelity analyses to increase the optimization efficiency. However, the cost of CFD analyses is still huge for aerodynamic optimization due to multiple design variables, multi-optimal and strong nonlinearities. To solve this problem, a two-stage aerodynamic optimization method based on early termination of CFD convergence and variable-fidelity model is proposed. In the first optimization stage, the solutions by early termination CFD convergence and the convergenced CFD solutions are regarded as low-and high-fidelity data respectively for building variable-fidelity model. Then, the multi-island genetic algorithm is used in the global optimization based on the built variable-fidelity model. The modeling efficiency can be greatly improved due to many cheap low-fidelity data. In the second stage optimization, the global optimum from the first optimization stage is treated as the start of the Hooke-Jeeves algorithm to search locally based on convergenced CFD computations in order to acquire better-optimum. The proposed method is utilized in optimizing the aerodynamic performance of the airfoil of little UAV, and is compared with the EGO method based on single-fidelity Kriging surrogate model. The results show that the present two-level aerodynamic optimization method consumes less time.

An improved aerodynamic performance optimization method of 3-D low Reynolds number rotor blade

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shuyi Zhang ◽  
Bo Yang
Keyword(s):  
Neural Network ◽  
Reynolds Number ◽  
Performance Optimization ◽  
Rotor Blade ◽  
Optimization Method ◽  
Aerodynamic Performance ◽  
Aerodynamic Optimization ◽  
Parametrization Method ◽  
Value Evaluation ◽  
Fitness Value

Abstract In this paper, an improved aerodynamic performance optimization method for 3-D low Reynolds number (Re) rotor blade is proposed. A conventional optimization procedure of blade is usually divided into three parts, such as the parameterization method, the fitness value evaluation and the optimization algorithm. This work is mainly focused on the first two parts. The parametrization method, Camber-FFD, is presented based on the camber parametrization method and the free-form deformation algorithm (FFD). The shape of 3-D blade is parameterized by the incidence angles and the coordinates of the maximum camber points. The fitness value evaluation has been realized with the help of an adaptive topological back propagation multi-layer forward artificial neural network (BP-MLFANN). During the training of BP-MLFANN, the hybrid particle swarm optimization method combined with the modified very fast simulate annealing algorithm (HPSO-MVFSA) is adopted to determine the neural network topology adaptively. To verify the effectiveness of this aerodynamic optimization method, the aerodynamic performance of a 3-D low-Re blade, such as Blade D900, is optimized, and the results are compared and analyzed based on the experiments and simulations. It is proved that this aerodynamic optimization method is feasible.

A parallel multi-fidelity optimization approach in induction hardening

2019 ◽  
Vol 39 (1) ◽  
pp. 133-143
Author(s):  
Marco Baldan ◽  
Alexander Nikanorov ◽  
Bernard Nacke
Keyword(s):  
Parallel Computing ◽  
Optimization Technique ◽  
Computational Cost ◽  
Induction Hardening ◽  
Computational Time ◽  
High Fidelity ◽  
Optimization Approach ◽  
Content Type ◽  
Fidelity Model ◽  
Hardening System

Purpose Reliable modeling of induction hardening requires a multi-physical approach, which makes it time-consuming. In designing an induction hardening system, combining such model with an optimization technique allows managing a high number of design variables. However, this could lead to a tremendous overall computational cost. This paper aims to reduce the computational time of an optimal design problem by making use of multi-fidelity modeling and parallel computing. Design/methodology/approach In the multi-fidelity framework, the “high-fidelity” model couples the electromagnetic, thermal and metallurgical fields. It predicts the phase transformations during both the heating and cooling stages. The “low-fidelity” model is instead limited to the heating step. Its inaccuracy is counterbalanced by its cheapness, which makes it suitable for exploring the design space in optimization. Then, the use of co-Kriging allows merging information from different fidelity models and predicting good design candidates. Field evaluations of both models occur in parallel. Findings In the design of an induction heating system, the synergy between the “high-fidelity” and “low-fidelity” model, together with use of surrogates and parallel computing could reduce up to one order of magnitude the overall computational cost. Practical implications On one hand, multi-physical modeling of induction hardening implies a better understanding of the process, resulting in further potential process improvements. On the other hand, the optimization technique could be applied to many other computationally intensive real-life problems. Originality/value This paper highlights how parallel multi-fidelity optimization could be used in designing an induction hardening system.

Variable Fidelity Modeling in Closed Loop Dynamical Systems

2014 ◽  
Author(s):  
Matthew A. Williams ◽  
Andrew G. Alleyne
Keyword(s):  
Dynamical Systems ◽  
Closed Loop ◽  
System Development ◽  
Computational Cost ◽  
Computational Effort ◽  
High Fidelity ◽  
Vapor Compression System ◽  
Variable Fidelity ◽  
Time Required ◽  
Fidelity Model

In the early stages of control system development, designers often require multiple iterations for purposes of validating control designs in simulation. This has the potential to make high fidelity models undesirable due to increased computational complexity and time required for simulation. As a solution, lower fidelity or simplified models are used for initial designs before controllers are tested on higher fidelity models. In the event that unmodeled dynamics cause the controller to fail when applied on a higher fidelity model, an iterative approach involving designing and validating a controller’s performance may be required. In this paper, a switched-fidelity modeling formulation for closed loop dynamical systems is proposed to reduce computational effort while maintaining elevated accuracy levels of system outputs and control inputs. The effects on computational effort and accuracy are investigated by applying the formulation to a traditional vapor compression system with high and low fidelity models of the evaporator and condenser. This sample case showed the ability of the switched fidelity framework to closely match the outputs and inputs of the high fidelity model while decreasing computational cost by 32% from the high fidelity model. For contrast, the low fidelity model decreases computational cost by 48% relative to the high fidelity model.

A Variable Fidelity Model Management Framework for Multiscale Computational Design of Continuous Fiber SiC-Si3N4 Ceramic Composites

2007 ◽  
Author(s):  
Gilberto Meji´a Rodri´guez ◽  
John E. Renaud ◽  
Vikas Tomar
Keyword(s):  
Material Design ◽  
Ceramic Composites ◽  
Design Tool ◽  
Material Behavior ◽  
Model Management ◽  
High Fidelity ◽  
Management Framework ◽  
Design Cycle ◽  
Variable Fidelity ◽  
Fidelity Model

Research applications involving design tool development for multiple phase material design are at an early stage of development. The computational requirements of advanced numerical tools for simulating material behavior such as the finite element method (FEM) and the molecular dynamics method (MD) can prohibit direct integration of these tools in a design optimization procedure where multiple iterations are required. The complexity of multiphase material behavior at multiple scales restricts the development of a comprehensive meta-model that can be used to replace the multiscale analysis. One, therefore, requires a design approach that can incorporate multiple simulations (multi-physics) of varying fidelity such as FEM and MD in an iterative model management framework that can significantly reduce design cycle times. In this research a material design tool based on a variable fidelity model management framework is presented. In the variable fidelity material design tool, complex “high fidelity” FEM analyses are performed only to guide the analytic “low-fidelity” model toward the optimal material design. The tool is applied to obtain the optimal distribution of a second phase, consisting of silicon carbide (SiC) fibers, in a silicon-nitride (Si3N4) matrix to obtain continuous fiber SiC-Si3N4 ceramic composites (CFCCs) with optimal fracture toughness. Using the variable fidelity material design tool in application to one test problem, a reduction in design cycle time around 80 percent is achieved as compared to using a conventional design optimization approach that exclusively calls the high fidelity FEM.

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.

An Aerodynamic Optimization Procedure for the Preliminary Design of Centrifugal Compressor Stages

2008 ◽  
Author(s):  
Omar Elshamy ◽  
Nidal Ghizawi ◽  
Ce´line Yon ◽  
Simone Pazzi ◽  
Denis Guenard
Keyword(s):  
Centrifugal Compressor ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Computational Time ◽  
Preliminary Design ◽  
Trial And Error ◽  
Aerodynamic Optimization ◽  
Peak Efficiency ◽  
Stage Design ◽  
Design Variables

This paper presents an automated aerodynamic optimization procedure for the preliminary design of centrifugal compressors. The proposed procedure interfaces a well-validated prediction tool with a GE in-house developed optimization code (PEZ). In GE Oil & Gas this tool is used to predict the performance of a single centrifugal compressor stage the outline of which requires more than thirty geometric parameters to be set. In the early phase of a new stage design, the designer manually varies all related parameters in the framework of a trial-and-error approach. The optimization procedure eliminates the inconvenience of a vast amount of manually launched simulations required by variations of the large number of design variables. Additionally, this procedure can perform trade-off studies and sensitivity analysis. In this case the optimization plan consists of a differential evolution (DE) genetic algorithm followed by a simplex-based optimization method (AMOEBA). The procedure was challenged with several existing designs by setting different objective/constraints combinations. The optimizer was often able to improve the predicted performance, as for an old 2D design where it was possible to increase the peak efficiency of approximately 2.6%. Also, the algorithm proved able to maximize the polytropic head (+12% with respect to baseline), while keeping unaltered both surge and choke limits. The computational time was about 40 hours per case on a Windows workstation (3.20 GHz, 3.5 GB RAM).

A Variable Fidelity Model Management Framework for Designing Multiphase Materials

2008 ◽  
Vol 130 (9) ◽  
Author(s):  
Gilberto Mejía-Rodríguez ◽  
John E. Renaud ◽  
Vikas Tomar
Keyword(s):  
Material Design ◽  
Design Tool ◽  
Model Management ◽  
High Fidelity ◽  
Management Framework ◽  
Cycle Times ◽  
Design Cycle ◽  
Numerical Tools ◽  
Variable Fidelity ◽  
Fidelity Model

Research applications involving design tool development for multi phase material design are at an early stage of development. The computational requirements of advanced numerical tools for simulating material behavior such as the finite element method (FEM) and the molecular dynamics (MD) method can prohibit direct integration of these tools in a design optimization procedure where multiple iterations are required. One, therefore, requires a design approach that can incorporate multiple simulations (multiphysics) of varying fidelity such as FEM and MD in an iterative model management framework that can significantly reduce design cycle times. In this research a material design tool based on a variable fidelity model management framework is presented. In the variable fidelity material design tool, complex “high-fidelity” FEM analyses are performed only to guide the analytic “low-fidelity” model toward the optimal material design. The tool is applied to obtain the optimal distribution of a second phase, consisting of silicon carbide (SiC) fibers, in a silicon-nitride (Si3N4) matrix to obtain continuous fiber SiC–Si3N4 ceramic composites with optimal fracture toughness. Using the variable fidelity material design tool in application to two test problems, a reduction in design cycle times of between 40% and 80% is achieved as compared to using a conventional design optimization approach that exclusively calls the high-fidelity FEM. The optimal design obtained using the variable fidelity approach is the same as that obtained using the conventional procedure. The variable fidelity material design tool is extensible to multiscale multiphase material design by using MD based material performance analyses as the high-fidelity analyses in order to guide low-fidelity continuum level numerical tools such as the FEM or finite-difference method with significant savings in the computational time.

Design strategies for multi-objective optimization of aerodynamic surfaces

2017 ◽  
Vol 34 (5) ◽  
pp. 1724-1753 ◽  
Author(s):  
Anand Amrit ◽  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Pareto Front ◽  
Design Space ◽  
Optimization Problems ◽  
Computational Time ◽  
High Fidelity ◽  
Design Strategies ◽  
Aerodynamic Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Aerodynamic Surfaces

Purpose This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level. Design/methodology/approach Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate. Findings Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day. Originality/value The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

scholarly journals Variable Fidelity Modeling as Applied to Trajectory Optimization for a Hydraulic Backhoe

2009 ◽  
Author(s):  
Roxanne A. Moore ◽  
Christiaan J. J. Paredis
Keyword(s):  
Trajectory Optimization ◽  
Design Space Exploration ◽  
Design Space ◽  
Optimization Process ◽  
High Fidelity ◽  
Simulation And Optimization ◽  
Variable Fidelity ◽  
Computational Resources ◽  
The Cost ◽  
Fidelity Model

Modeling, simulation, and optimization play vital roles throughout the engineering design process; however, in many design disciplines the cost of simulation is high, and designers are faced with a tradeoff between the number of alternatives that can be evaluated and the accuracy with which they are evaluated. In this paper, a methodology is presented for using models of various levels of fidelity during the optimization process. The intent is to use inexpensive, low-fidelity models with limited accuracy to recognize poor design alternatives and reserve the high-fidelity, accurate, but also expensive models only to characterize the best alternatives. Specifically, by setting a user-defined performance threshold, the optimizer can explore the design space using a low-fidelity model by default, and switch to a higher fidelity model only if the performance threshold is attained. In this manner, the high fidelity model is used only to discern the best solution from the set of good solutions, so computational resources are conserved until the optimizer is close to the solution. This makes the optimization process more efficient without sacrificing the quality of the solution. The method is illustrated by optimizing the trajectory of a hydraulic backhoe. To characterize the robustness and efficiency of the method, a design space exploration is performed using both the low and high fidelity models, and the optimization problem is solved multiple times using the variable fidelity framework.

Multi-fidelity modelling of mixed convection based on experimental correlations and numerical simulations

2016 ◽  
Vol 809 ◽  
pp. 895-917 ◽  
Author(s):  
H. Babaee ◽  
P. Perdikaris ◽  
C. Chryssostomidis ◽  
G. E. Karniadakis
Keyword(s):  
Reynolds Number ◽  
Nusselt Number ◽  
Natural Convection ◽  
Mixed Convection ◽  
Numerical Simulations ◽  
Response Surface ◽  
Forced Convection ◽  
High Fidelity ◽  
Variable Fidelity ◽  
Fidelity Model

For thermal mixed-convection flows, the Nusselt number is a function of Reynolds number, Grashof number and the angle between the forced- and natural-convection directions. We consider flow over a heated cylinder for which there is no universal correlation that accurately predicts Nusselt number as a function of these parameters, especially in opposing-convection flows, where the natural convection is against the forced convection. Here, we revisit this classical problem by employing modern tools from machine learning to develop a general multi-fidelity framework for constructing a stochastic response surface for the Nusselt number. In particular, we combine previously developed experimental correlations (low-fidelity model) with direct numerical simulations (high-fidelity model) using Gaussian process regression and autoregressive stochastic schemes. In this framework the high-fidelity model is sampled only a few times, while the inexpensive empirical correlation is sampled at a very high rate. We obtain the mean Nusselt number directly from the stochastic multi-fidelity response surface, and we also propose an improved correlation. This new correlation seems to be consistent with the physics of this problem as we correct the vectorial addition of forced and natural convection with a pre-factor that weighs differently the forced convection. This, in turn, results in a new definition of the effective Reynolds number, hence accounting for the ‘incomplete similarity’ between mixed convection and forced convection. In addition, due to the probabilistic construction, we can quantify the uncertainty associated with the predictions. This information-fusion framework is useful for elucidating the physics of the flow, especially in cases where anomalous transport or interesting dynamics may be revealed by contrasting the variable fidelity across the models. While in this paper we focus on the thermal mixed convection, the multi-fidelity framework provides a new paradigm that could be used in many different contexts in fluid mechanics including heat and mass transport, but also in combining various levels of fidelity of models of turbulent flows.

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