Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles

Luo, Changtong; Hu, Zongmin; Zhang, Shao‐Liang; Jiang, Zonglin

doi:10.1016/j.engappai.2015.09.001

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…On the other hand, within the machine learning field, there have been also some application of models based on ANNs or support vector machines (SVMs) for aerodynamic coefficient predictions [16][17][18], aerodynamic design [19][20][21][22] and uncertainty quantification or/and robust design [23,24]. Other supervised learning methods, such as Bayesian automatic relevance determination (ARD) regression or Bayesian ridge have been applied in [25][26][27] mainly for aerodynamic design and optimization and decision tree-based models have been used in [28,29].…”

Section: Brief Review Of the State-of-the-artmentioning

confidence: 99%

On the application of surrogate regression models for aerodynamic coefficient prediction

Andrés-Pérez

Paulete-Periáñez

2021

Complex Intell. Syst.

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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.

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Section: Brief Review Of the State-of-the-artmentioning

confidence: 99%

On the application of surrogate regression models for aerodynamic coefficient prediction

Andrés-Pérez

Paulete-Periáñez

2021

Complex Intell. Syst.

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“…Data-driven modeling of complex systems has become increasingly important for industrial data analysis when the experimental model structure is unknown or wrong, or the concerned system has changed [1,2]. Symbolic regression aims to find a data-driven model that can describe a given system based on observed input-response data, and plays an important role in different areas of engineering such as signal processing [3], system identification [4], industrial data analysis [5], and industrial design [6]. Unlike conventional regression methods that require a mathematical model of a given form, symbolic regression is a datadriven approach to extract an appropriate model from a space of all possible expressions S models are out of the scope of the separable model (Eq.…”

Section: Introductionmentioning

confidence: 99%

Block building programming for symbolic regression

2018

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a b s t r a c tSymbolic regression that aims to detect underlying data-driven models has become increasingly important for industrial data analysis. For most existing algorithms such as genetic programming (GP), the convergence speed might be too slow for large-scale problems with a large number of variables. This situation may become even worse with increasing problem size. The aforementioned difficulty makes symbolic regression limited in practical applications. Fortunately, in many engineering problems, the independent variables in target models are separable or partially separable. This feature inspires us to develop a new approach, block building programming (BBP). BBP divides the original target function into several blocks, and further into factors. The factors are then modeled by an optimization engine (e.g. GP). Under such circumstances, BBP can make large reductions to the search space. The partition of separability is based on a special method, block and factor detection. Two different optimization engines are applied to test the performance of BBP on a set of symbolic regression problems. Numerical results show that BBP has a good capability of structure and coefficient optimization with high computational efficiency.

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“…Data-driven modeling has become a powerful technique in different areas of engineering, such as industrial data analysis (Luo et al, 2015;Li et al, 2017), circuits analysis and design (Ceperic et al, 2014;Shokouhifar & Jalali, 2015;Zarifi et al, 2015), signal processing (Yang et al, 2005;Volaric et al, 2017), empirical modeling (Gusel & Brezocnik, 2011;Mehr & Nourani, 2017), system identification (Guo & Li, 2012;Wong et al, 2008), etc. For a concerned data-driven modeling problem with n input variables, we aim to find a performance function f * : R n → R that best explains the relationship between input variables x = x 1 x 2 · · · x n T ∈ R n and the target system (or constrained system) based on a given set of sample points S = x (1) x (2) · · · x (N ) T ∈ R N ×n .…”

Section: Introductionmentioning

confidence: 99%

A multilevel block building algorithm for fast modeling generalized separable systems

Chen

Luo

Jiang

2018

Expert Systems with Applications

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Data-driven modeling plays an increasingly important role in different areas of engineering. For most of existing methods, such as genetic programming (GP), the convergence speed might be too slow for large scale problems with a large number of variables. It has become the bottleneck of GP for practical applications. Fortunately, in many applications, the target models are separable in some sense. In this paper, we analyze different types of separability of some real-world engineering equations and establish a mathematical model of generalized separable system (GS system). In order to get the structure of the GS system, a multilevel block building (MBB) algorithm is proposed, in which the target model is decomposed into a number of blocks, further into minimal blocks and factors. Compare to the conventional GP, MBB can make large reductions to the search space. This makes MBB capable of modeling a complex system. The minimal blocks and factors are optimized and assembled with a global optimization search engine, low dimensional simplex evolution (LDSE). An extensive study between the proposed MBB and a state-of-the-art data-driven fitting tool, Eureqa, has been presented with several man-made problems, as well as some real-world problems. Test results indicate that the proposed method is more effective and efficient under all the investigated cases.

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Adaptive space transformation: An invariant based method for predicting aerodynamic coefficients of hypersonic vehicles

Cited by 11 publications

References 15 publications

On the application of surrogate regression models for aerodynamic coefficient prediction

On the application of surrogate regression models for aerodynamic coefficient prediction

Block building programming for symbolic regression

A multilevel block building algorithm for fast modeling generalized separable systems

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