1,720,973 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
The Use of Hijama (Wet Cupping) in Alternative and Complementary Medicine: Efficacious or Perilous?
No full textOptimization of the Shape of a Hydrokinetic Turbine's Draft Tube and Hub Assembly Using Design-by-Morphing with Bayesian Optimization
Full text linkFinding 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
Airfoil Optimization using Design-by-Morphing
Full text linkWe 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
Effect of TiB2 on the Mechanical and Tribological Properties of Marine Grade Aluminum Alloy 5052: An Experimental Investigation
Full text linkAluminum Alloy 5052 is one of the excellent corrosion resistant alloys among the Aluminum 5000 series alloys. Despite the favourable corrosion resistance, it exhibits low resistance to wear and perform poorly in various tribological applications.This study attempts to investigate the impact of TiB2 reinforcement on the mechanical and tribological properties of Aluminum Alloy 5052 matrix alloy, fabricated using the stir casting technique. Composites with varying weight percentages (0, 2.5, 5 and 7.5 wt. %)of TiB2 were prepared. Vicker's hardness testing was conducted to assess the effect of TiB2 content on composite hardness.Hardness of composites increased from 65.5 to 85.16 HV in tandem with the TiB2 content depicting a rise of about 30 %. Tribological properties were studied through dry sliding wear tests using a pin-on-disc tribometer, with varying loads (10 N and 50 N)and sliding speeds (1 m/s and 3 m/s). The results demonstrate that the addition of TiB2 significantly improves the wear resistance of the composites,being optimum in composite with 5 wt. % of TiB2. A maximum increase of about 36 % in wear resistance was observed at high load and high-speed conditions corresponding to 5 wt. % TiB2 concentration. FESEM analysis revealed distinct wear modes and the role of TiB2 in changing the wear mechanism from abrasion to delamination. This study underscores the pivotal role of TiB2 in enhancing the mechanical and tribological properties of Aluminum Alloy5052, expanding its application scope, particularly in lightweight engineering applications in general and marine in particular
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
Bayesian-Optimized Riblet Surface Design for Turbulent Drag Reduction via Design-by-Morphing with Large Eddy Simulation
Full text linkAbstract:
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
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