13 research outputs found
Airfoil optimization using Design-by-Morphing with minimized design-space dimensionality
No full textEffective airfoil geometry optimization requires exploring a diverse range ofdesigns using as few design variables as possible. This study introduces AirDbM, a Design-by-Morphing (DbM) approach specialized for airfoil optimization that systematically reduces design-space dimensionality. AirDbM selects an optimal set of 12 baseline airfoils from the UIUC airfoil database, which contains over 1,600 shapes, by sequentially adding the baseline that most increases the design capacity. With these baselines, AirDbM reconstructs 99% of the database with a mean absolute error below 0.005, which matches the performance of a previous DbM approach that used more baselines. In multi-objective aerodynamic optimization, AirDbM demonstrates rapid convergence and achieves a Pareto front with a greater hypervolume than that of the previous larger-baseline study, where new Pareto optimal solutions are discovered with enhanced lift-to-drag ratios at moderate stall tolerances. Furthermore, AirDbM demonstrates outstanding adaptability for reinforcement learning (RL) agents in generating airfoil geometry when compared to conventional airfoil parameterization methods, implying the broader potential of DbM in machine learning-driven design
Bayesian Optimization For Multi-Objective Mixed-Variable Problems
No full textOptimizing multiple, non-preferential objectives for mixed-variable,
expensive black-box problems is important in many areas of engineering and
science. The expensive, noisy, black-box nature of these problems makes them
ideal candidates for Bayesian optimization (BO). Mixed-variable and
multi-objective problems, however, are a challenge due to BO's underlying
smooth Gaussian process surrogate model. Current multi-objective BO algorithms
cannot deal with mixed-variable problems. We present MixMOBO, the first
mixed-variable, multi-objective Bayesian optimization framework for such
problems. Using MixMOBO, optimal Pareto-fronts for multi-objective,
mixed-variable design spaces can be found efficiently while ensuring diverse
solutions. The method is sufficiently flexible to incorporate different kernels
and acquisition functions, including those that were developed for
mixed-variable or multi-objective problems by other authors. We also present
HedgeMO, a modified Hedge strategy that uses a portfolio of acquisition
functions for multi-objective problems. We present a new acquisition function,
SMC. Our results show that MixMOBO performs well against other mixed-variable
algorithms on synthetic problems. We apply MixMOBO to the real-world design of
an architected material and show that our optimal design, which was
experimentally fabricated and validated, has a normalized strain energy density
times greater than existing structures
Computational fluid dynamics analysis of a modified Savonius rotor and optimization using response surface methodology
No full text This article aims to present a two-dimensional parametric analysis of a modified Savonius wind turbine using computational fluid dynamics. The effects of three independent parameters of the rotor, namely, shape factor, overlap ratio, and tip speed ratio on turbine performance were studied and then optimized for maximum coefficient of performance using response surface methodology. The rotor performance was analyzed over specific domains of the parameters under study, and three-variable Box-Behnken design was used for design of experiment. The specific parametric combinations as per design of experiment were simulated using ANSYS Fluent®, and the response variable, coefficient of performance (Cp), was calculated. The sliding mesh model was utilized, and the flow was simulated using Shear Stress Transport (SST) k − ω model. The model was validated using past experimental results and found to predict parametric effects accurately. Minitab® and ReliaSoft DOE++® were used to develop regression equation and find the optimum combination of parameters for coefficient of performance over the specified parametric domains using response surface methodology. </jats:p
Airfoil Optimization using Design-by-Morphing
No full textWe present Design-by-Morphing (DbM), a novel design methodology applicable to
creating a search space for topology optimization of 2D airfoils. Most design
techniques impose geometric constraints and sometimes designers' bias on the
design space itself, thus restricting the novelty of the designs created, and
only allowing for small local changes. We show that DbM methodology does not
impose any such restrictions on the design space and allows for extrapolation
from the search space, thus granting truly radical and large search space with
a few design parameters. In comparison to other shape design methodologies, we
apply DbM to create a search space for 2D airfoils. We optimize this airfoil
shape design space for maximizing the lift-over-drag ratio, , and
stall angle tolerance, . Using a bi-objective genetic algorithm
to optimize the DbM space, it is found that we create a Pareto-front of radical
airfoils exhibiting remarkable properties for both objectives
Optimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization
No full textFinding the optimal design of a hydrodynamic or aerodynamic surface is often
impossible due to the expense of evaluating the cost functions (say, with
computational fluid dynamics) needed to determine the performances of the flows
that the surface controls. In addition, inherent limitations of the design
space itself due to imposed geometric constraints, conventional
parameterization methods, and user bias can restrict {\it all} of the designs
within a chosen design space regardless of whether traditional optimization
methods or newer, data-driven design algorithms with machine learning are used
to search the design space. We present a 2-pronged attack to address these
difficulties: we propose (1) a methodology to create the design space using
morphing that we call {\it Design-by-Morphing} (DbM); and (2) an optimization
algorithm to search that space that uses a novel Bayesian Optimization (BO)
strategy that we call {\it Mixed variable, Multi-Objective Bayesian
Optimization} (MixMOBO). We apply this shape optimization strategy to maximize
the power output of a hydrokinetic turbine. Applying these two strategies in
tandem, we demonstrate that we can create a novel, geometrically-unconstrained,
design space of a draft tube and hub shape and then optimize them
simultaneously with a {\it minimum} number of cost function calls. Our
framework is versatile and can be applied to the shape optimization of a
variety of fluid problems
Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing with Large Eddy Simulation
No full textAbstract:
A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing Design-by-Morphing (DbM), Large Eddy Simulation (LES), and Bayesian Optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of 3 optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8 %. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity of computational design before materialization
Systematic design of Cauchy symmetric structures through Bayesian optimization
No full textUsing a new Bayesian Optimization algorithm to guide the design of mechanical metamaterials, we design nonhomogeneous 3D structures possessing the Cauchy symmetry, which dictates the relationship between continuum and atomic deformations. Recent efforts to merge optimization techniques with the design of mechanical metamaterials has resulted in a concentrated effort to tailor their elastic and post elastic properties. Even though these properties of either individual unit cells or homogenized continua can be simulated using multi-physics solvers and well established optimization schemes, they are often computationally expensive and require many design iterations, rendering the validation stage a significant obstacle in the design of new metamaterial designs. This study aims to provide a framework on how to utilize miniscule computational cost to control the elastic properties of metamaterials such that specific symmetries can be accomplished. Using the Cauchy symmetry as a design objective, we engineer structures through the strategic arrangement of 5 different unit cells in a 5 × 5 × 5 cubic symmetric microlattice structure. This lattice design, despite constituting a design space with 510 3D lattice configurations, can converge to an effective solution in only 69 function calls as a result of the efficiency of the new Bayesian optimization scheme. To validate the mechanical behavior of the design, the lattice structures were fabricated using multiphoton lithography and mechanically tested, revealing a close correlation between experiments and simulated results in the elastic regime. Ultimately, a similar methodology can be utilized to design metamaterials with other material properties, aspiring to control properties at different length scales, an endeavor that requires inordinate computation cost
Strength through defects: A novel Bayesian approach for the optimization of architected materials
No full textWe use a previously unexplored Bayesian optimization framework, “evolutionary Monte Carlo sampling,” to systematically design the arrangement of defects in an architected microlattice to maximize its strain energy density before undergoing catastrophic failure. Our algorithm searches a design space with billions of 4 × 4 × 5 3D lattices, yet it finds the global optimum with only 250 cost function evaluations. Our optimum has a normalized strain energy density 12,464 times greater than its commonly studied defect-free counterpart. Traditional optimization is inefficient for this microlattice because (i) the design space has discrete, qualitative parameter states as input variables, (ii) the cost function is computationally expensive, and (iii) the design space is large. Our proposed framework is useful for architected materials and for many optimization problems in science and elucidates how defects can enhance the mechanical performance of architected materials
Mixed-Variable Multi-Objective Bayesian Optimization, Design-by-Morphing and their Applications
No full textFluid flows are non-intuitive. Even with years of experience, non-intuitive behavior of fluids can mean the optimal geometry of fluid machinery is surprising or even extreme (consider, for instance, the bulbous bow of a ship). Finding the optimal design of a hydrodynamic or aerodynamic surfaces is often impossible due to the expense of evaluating the cost functions (say, with computational fluid dynamics) needed to determine the performances of the flows that the surface controls. In addition, inherent limitations of the design space itself due to imposed geometric constraints, conventional parameterization methods, and user bias can restrict all of the designs within a chosen design space regardless of whether traditional optimization methods or newer, data-driven design algorithms with machine learning are used to search the design space. This dissertation presents two methodologies to address these difficulties: (1) Design-by-Morphing (DbM), a novel strategy for creating a design search space by morphing homeomorphic shapes to create a continuous and constraint-free design search space that can produce radical extrapolated shapes, something which is unique from existing design strategies; and (2) an optimization algorithm to search that space that uses a novel Mixed-variable, Multi-Objective Bayesian Optimization that we call MixMOBO, that can optimize such expensive, black-box problems with minimum number of functions calls. We apply these methodologues for optimization of several problems and present shape optimization of airfoils, draft-tubes for hydrokinetic turbines, and architected meta-materials. In all cases, we show significantly improved and radical designs.Chapter One of this thesis focuses on the details of the MixMOBO algorithm, the first mixed-variable, multi-objective Bayesian optimization algorithm. MixMOBO outperforms existing algorithms for mixed-variable problems. It details HedgeMO strategy for hedging acquisition function portfolios for multi-objective problems. MixMOBO is then applied for optimization of strain energy density of an architected meta-material structure with categorical variables. From a design space of 8.5 billion possible candidates, our algorithm is able to optimize the design space with only 250 function evaluation and achieve 10^4 times improvement in strain energy density over existing structures [1,2]. Chapter Two focuses on applying MixMOBO for design of Cauchy-Symmetric architected meta-material structures. With only 69 function calls, MixMOBO is able to find such a structure from a design space of 10^7 possible structures. Chapter Three demonstrates the use of Design-by-Morphing for optimization of airfoils. We show that with just 25 baseline shapes, we are able to reproduce the UIUC airfoil database with high fidelity and optimize this space to create aerodynamically superior and safer airfoils [3]. Chapter Four focuses on application of design of a draft tube for a hydrokinetic turbine to maximize pressure recovery at the exit of the turbine [4]
