Esther Andres

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using β-variational autoencoder (β-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The β-VAE model, enhanced with principal component analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through β requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian process regression is used to predict latent space variables from flight conditions, showing robust behavior independent of β, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on β and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of β-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction.

Computational fluid dynamics (CFD) simulations are nowadays been intensively used in aeronautical industries to analyse the aerodynamic performance of different aircraft configurations within a design process. These simulations allow to reduce time and cost compared to wind tunnel experiments or flight tests. However, for complex configurations, CFD simulations may still take several hours using high-performance computers to deliver results. For this reason, surrogate models are currently starting to be considered as a substitute of the CFD tool with a reasonable prediction. This paper presents a review on surrogate regression models for aerodynamic coefficient prediction, in particular for the prediction of lift and drag coefficients. To compare the behaviour of the regression models, three different aeronautical configurations have been used, a NACA0012 airfoil, a RAE2822 airfoil and 3D DPW wing. These databases are also freely provided to the scientific community to allow other researchers to make further comparison with other methods.

Machine learning and data mining techniques are nowadays being used in many business sectors to exploit the data in order to detect trends, discover certain features and patters, or even predict the future. However, in the field of aerodynamics, the application of these techniques is still in the initial stages. This paper focuses on exploring the benefits that machine learning and data mining techniques can offer to aerodynamicists in order to extract knowledge from the CFD data and to make quick predictions of aerodynamic coefficients. For this purpose, three aerodynamic databases (NACA0012 airfoil, RAE2822 airfoil and 3D DPW wing) have been used and results show that machine-learning and data-mining techniques have a huge potential also in this field.

SUMMARY: This paper considers the computation of flow sensitivities that arise in the context of design optimization. The scheme is based on the solution of a continuous adjoint problem, for which two complementary, although analytically equivalent, approaches have been routinely used for some time now, yielding expressions for the sensitivities that contain, respectively, boundary and domain integrals. These concepts are clarified in a unified framework and their equivalence at the continuous level is demonstrated through appropriate algebraic manipulations. Equivalence at the discrete level is assessed through numerical testing for various aerodynamic shape‐optimization problems. Copyright © 2011 John Wiley & Sons, Ltd.

This paper justifies the use of estimation and prediction of carries to increase the performance of functional units built with the replication of full adders while keeping a low area penalization. Adders and multipliers are the most representative modules in this group of functional units. The use of these design techniques allows the implementation of modules with performance improvements ranging from 20% to 50% with only an area overheads around 5%. These functional units are suitable for asynchronous circuits but they could also be introduced in synchronous circuits with speculative techniques. The basic idea consists in estimating the carry out from some parts of the functional units, allowing every part to operate independently and in parallel. These modules are connected to build bigger ones. Results from simulations show that for some applications it is possible to make predictions even more accurate that the bit-based estimation. Predictions have also the advantage they can be introduced in the multipliers design, whether estimators cannot. These predictions are similar to the ones used in the branch prediction in a processor.