Parametric model for the reconstruction and representation of hydrofoils and airfoils

Κώστας, Κωνσταντίνος; Amiralin, Azat; Sagimbayev, Sagi; Massalov, T.; Kalel, Yeraly; Politis, C. G.

doi:10.1016/j.oceaneng.2020.107020

Cited by 18 publications

(14 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[2] and subsequently extended to encompass a broader range of designs in Refs. [36,37], has been extensively used in the generation of D 1 . The parametric model proposed by Kostas et al in Ref.…”

Section: Parametric Modelmentioning

confidence: 99%

Generative vs. Non-Generative Models in Engineering Shape Optimization

Masood,

Usama,

Khan

et al. 2024

JMSE

View full text Add to dashboard Cite

Generative models offer design diversity but tend to be computationally expensive, while non-generative models are computationally cost-effective but produce less diverse and often invalid designs. However, the limitations of non-generative models can be overcome with the introduction of augmented shape signature vectors (SSVs) to represent both geometric and physical information. This recent advancement has inspired a systematic comparison of the effectiveness and efficiency of generative and non-generative models in constructing design spaces for novel and efficient design exploration and shape optimization, which is demonstrated in this work. These models are showcased in airfoil/hydrofoil design, and a comparison of the resulting design spaces is conducted in this work. A conventional generative adversarial network (GAN) and a state-of-the-art generative model, the performance-augmented diverse generative adversarial network (PaDGAN), are juxtaposed with a linear non-generative model based on the coupling of the Karhunen–Loève Expansion and a physics-informed shape signature vector (SSV-KLE). The comparison demonstrates that, with an appropriate shape encoding and a physics-augmented design space, non-generative models have the potential to cost-effectively generate high-performing valid designs with enhanced coverage of the design space. In this work, both approaches were applied to two large foil profile datasets comprising real-world and artificial designs generated through either a profile-generating parametric model or a deep-learning approach. These datasets were further enriched with integral properties of their members’ shapes, as well as physics-informed parameters. The obtained results illustrate that the design spaces constructed by the non-generative model outperform the generative model in terms of design validity, generating robust latent spaces with no or significantly fewer invalid designs when compared to generative models. The performance and diversity of the generated designs were compared to provide further insights about the quality of the resulting spaces. These findings can aid the engineering design community in making informed decisions when constructing design spaces for shape optimization, as it has been demonstrated that, under certain conditions, computationally inexpensive approaches can closely match or even outperform state-of-the art generative models.

show abstract

Section: Parametric Modelmentioning

confidence: 99%

Generative vs. Non-Generative Models in Engineering Shape Optimization

Masood,

Usama,

Khan

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…We test the developed method in two constant-section wing cases: a rectangular unswept wing and a swept one with a 20 o sweep angle. Both wings have a semi-span of length s = 3m, a chord of length c = 1m and are generated by translating a NACA0012 profile, represented via the parameterization described in [47]. The flow is uniform along the x-axis and the angle of attack is equal to 6.75 o .…”

Section: Wings With Constant Naca0012 Airfoil Sectionmentioning

confidence: 99%

An Isogeometric Boundary Element Method for 3D lifting flows using T-splines

Chouliaras

Kaklis

Κώστας

et al. 2021

Computer Methods in Applied Mechanics and Engineering

Self Cite

View full text Add to dashboard Cite

“…Control points are commonly employed when performing local and/or global shape modifications of the constituent surface patches and quite naturally they become the obvious selection when considering design variables in the context of design optimisation. However, control points usage as design variables does not come without problems, and higher level parameters are preferable and generally more efficient in formulating and solving shape optimisations problems; see [7][8][9]. Therefore, NURBS-based IGA has become a favourable approach for simulation-driven shape optimisation as all involved stages, i.e., design, parameterisation, and analysis, employ the same geometry representation.…”

Section: Introductionmentioning

confidence: 99%

Bayesian shape optimisation in high dimensional design spaces using isogeometric boundary element analysis

Khan

Kaklis

Κώστας

et al. 2023

AIAA SCITECH 2023 Forum

View full text Add to dashboard Cite

In this work, we employ dimensionality reduction and a Bayesian optimisation approach in an isogeometric analysis (IGA) setting to reduce the design space's dimensionality and ease its exploration while reducing the number of required design evaluations. In the first step, statistical dependencies implicit in the shape modification function encode essential latent features of the underlining shape while maintaining the maximum geometric variance. These latent features are used to form a low-dimensional design subspace with a correspondingly low-dimensional representation of the shape modification function. The subspace is then employed in design optimisation with a Bayesian approach. During space exploration, smooth surface representations are reconstructed from the discrete design instances of the subspace and evaluated with an IGA-enabled hydrodynamic solver. The proposed approach is demonstrated for a design optimisation of a naval ship-hull model, originally parameterised by 27 parameters, aiming at the minimisation of its wave-making resistance. The benefits of the proposed approach are contrasted with conventional optimisation procedures.

show abstract

Parametric model for the reconstruction and representation of hydrofoils and airfoils

Cited by 18 publications

References 22 publications

Generative vs. Non-Generative Models in Engineering Shape Optimization

Generative vs. Non-Generative Models in Engineering Shape Optimization

An Isogeometric Boundary Element Method for 3D lifting flows using T-splines

Bayesian shape optimisation in high dimensional design spaces using isogeometric boundary element analysis

Contact Info

Product

Resources

About