Aerodynamic parameter identification for hypersonic vehicles considering input measurement errors

2014 ◽  
Author(s):  
Hao Chen ◽  
Zhihua Xiong ◽  
Hao Ye ◽  
Yingdong Hong
Keyword(s):  
Parameter Identification ◽  
Measurement Errors ◽  
Hypersonic Vehicles ◽  
Aerodynamic Parameter

Aerodynamic parameter identification of hypersonic vehicle via Pigeon-inspired optimization

2017 ◽  
Vol 89 (3) ◽  
pp. 425-433 ◽  
Author(s):  
Qiang Xue ◽  
Duan Haibin
Keyword(s):  
Parameter Identification ◽  
Optimization Problem ◽  
Identification Problem ◽  
Optimization Method ◽  
Hypersonic Vehicles ◽  
Noisy Environment ◽  
Aerodynamic Parameter ◽  
Swarm Optimization ◽  
New Approach ◽  
Content Type

Purpose The purpose of this paper is to propose a new approach for aerodynamic parameter identification of hypersonic vehicles, which is based on Pigeon-inspired optimization (PIO) algorithm, with the objective of overcoming the disadvantages of traditional methods based on gradient such as New Raphson method, especially in noisy environment. Design/methodology/approach The model of hypersonic vehicles and PIO algorithm is established for aerodynamic parameter identification. Using the idea, identification problem will be converted into the optimization problem. Findings A new swarm optimization method, PIO algorithm is applied in this identification process. Experimental results demonstrated the robustness and effectiveness of the proposed method: it can guarantee accurate identification results in noisy environment without fussy calculation of sensitivity. Practical implications The new method developed in this paper can be easily applied to solve complex optimization problems when some traditional method is failed, and can afford the accurate hypersonic parameter for control rate design of hypersonic vehicles. Originality/value In this paper, the authors converted this identification problem into the optimization problem using the new swarm optimization method – PIO. This new approach is proved to be reasonable through simulation.

Electromagnetic and thermal parameter identification method for best prediction of temperature distribution on transformer tank covers

2015 ◽  
Vol 34 (2) ◽  
pp. 485-495 ◽  
Author(s):  
Patricia Penabad Durán ◽  
Paolo Di Barba ◽  
Xose Lopez-Fernandez ◽  
Janusz Turowski
Keyword(s):  
Temperature Distribution ◽  
Measurement Error ◽  
Parameter Identification ◽  
Objective Function ◽  
Measurement Errors ◽  
Optimization Algorithms ◽  
Identification Problem ◽  
Content Type ◽  
Identification Method ◽  
Wide Range

Purpose – The purpose of this paper is to describe a parameter identification method based on multiobjective (MO) deterministic and non-deterministic optimization algorithms to compute the temperature distribution on transformer tank covers. Design/methodology/approach – The strategy for implementing the parameter identification process consists of three main steps. The first step is to define the most appropriate objective function and the identification problem is solved for the chosen parameters using single-objective (SO) optimization algorithms. Then sensitivity to measurement error of the computational model is assessed and finally it is included as an additional objective function, making the identification problem a MO one. Findings – Computations with identified/optimal parameters yield accurate results for a wide range of current values and different conductor arrangements. From the numerical solution of the temperature field, decisions on dimensions and materials can be taken to avoid overheating on transformer covers. Research limitations/implications – The accuracy of the model depends on its parameters, such as heat exchange coefficients and material properties, which are difficult to determine from formulae or from the literature. Thus the goal of the presented technique is to achieve the best possible agreement between measured and numerically calculated temperature values. Originality/value – Differing from previous works found in the literature, sensitivity to measurement error is considered in the parameter identification technique as an additional objective function. Thus, solutions less sensitive to measurement errors at the expenses of a degradation in accuracy are identified by means of MO optimization algorithms.

Parallel Interval Algorithm for the Parameter Identification of Linear Elastomechanical Systems

1995 ◽  
Author(s):  
Michael Oeljeklaus ◽  
H. Günther Natke
Keyword(s):  
Parameter Identification ◽  
Measurement Errors ◽  
Degrees Of Freedom ◽  
A Priori ◽  
Search Space ◽  
Information Measurement ◽  
Large Structures ◽  
Speed Up ◽  
Parallel Iterative ◽  
Priori Information

Abstract An interval analytical approach to parameter identification in the frequency domain of mathematical models for linear elasto-mechanical systems is described. A priori information, measurement errors and — if possible — unmeasurable degrees of freedom are modelled in terms of intervals. A parallel iterative update method — based on the interval analytical Gauss-Seidel method — is used to reduce the volume of the parameter search space initially given. The search for the global minimum of the WLS objective function using output residuals is performed on the reduced parameter space in a last step1. Subsystem identification and sub-model synthesis are used in the case of realistic models with a large number of degrees of freedom. Parallelization of the algorithm with respect to subsystems is applied in the case of large structures to reduce the amount of memory and to speed-up the computation. Test results for some simulations for a test structure are given in order to illustrate the method.

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