Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

Hossain, Altab; Rahman, Ataur; Hossen, J.; Iqbal, Asma; Zahirul, M.I.

doi:10.1016/j.ast.2010.12.003

Cited by 29 publications

(21 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fuzzification is a process which converts input data to degrees of membership by a lookup in one or several membership functions [16]. Membership functions are numerical functions corresponding to linguistic terms.…”

Section: Fuzzificationmentioning

confidence: 99%

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Ilangkumaran¹,

Sasikumar²,

Sakthivel

2015

Ain Shams Engineering Journal

View full text Add to dashboard Cite

Nano crystalline materials are an area of interest for the researchers all over the world due to its superior mechanical properties such as high strength and high hardness. But the cost of nano-crystals is high because of the complexity and cost incurred during its production. This paper focuses on the application of Taguchi method with Fuzzy logic for optimizing the machining parameters of nano-crystalline structured chips production in High Carbon Steel (HCS) through machining. An orthogonal array, multi-response performance index, signals to noise ratio and analysis of variance are used to study the machining process with multi-response performance characteristics. The machining parameters namely rake angle, depth of cut, heat treatment, feed and cutting velocity are optimized with considerations of the multi-response performance characteristics. Using the Taguchi and Fuzzy logic method optimum cutting conditions are identified in order to obtain the smallest nanocrystalline structure via machining. Ó 2015 Faculty of Engineering, Ain Shams University. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

show abstract

Section: Fuzzificationmentioning

confidence: 99%

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Ilangkumaran¹,

Sasikumar²,

Sakthivel

2015

Ain Shams Engineering Journal

View full text Add to dashboard Cite

show abstract

“…Fuzzification is a process which converts input data to degrees of membership by a lookup in one or several membership functions (Hossain et al 2011). Membership functions are numerical functions corresponding to linguistic terms.…”

Section: Fuzzificationmentioning

confidence: 99%

Development of fuzzy logic model to predict the engine performance of fish oil biodiesel with diethyl ether

Ilangkumaran¹,

Sakthivel

Kumar³

et al. 2013

International Journal of Ambient Energy

View full text Add to dashboard Cite

Multiple Inputs and Multiple Outputs (MIMO) fuzzy logic model is developed to predict the engine performance of fish oil biodiesel blended with diethyl ether. Engine performance and emission characteristics such as brake thermal efficiency, hydrocarbon, exhaust gas temperature, oxides of nitrogen (NO x ), carbon monoxide, Smoke and carbon dioxide were considered. Experimental investigations on single-cylinder constant-speed direct-injection diesel engine are carried out under variable load conditions. The performance and emission characteristics are measured using an exhaust gas analyser, smoke meter, piezoelectric pressure transducer and crank angle encoder for different fuel blends and engine load conditions. In this model, triangular membership function is used to predict the performance. Computational results clearly demonstrated that the proposed fuzzy models produced less deviations and exhibited higher predictive accuracy with acceptable determination correlation coefficients of 0.96-0.99 with experimental values. The developed model produces the idealised results and has been found to be useful for predicting the engine performance and emission characteristics with limited number of available data.

show abstract

“…Aircraft nonlinear and unsteady aerodynamics has several independent causes that act in a symbiotic manner. Among them, unsteady upwash and downwash, dynamic stall, edge-separated vortex lag, and unsteady environmental disturbances (including wind and turbulence) are highlighted [12][13][14][15].…”

Section: Meridian Unmanned Aerial System Modelingmentioning

confidence: 99%

“…The neural partial differentiation was used as an alternative to the Delta method. A robust and versatile aerodynamic modeling has also been achieved using fuzzy logic modeling; see [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Garcia

Keshmiri

2013

Intl J Robust & Nonlinear

View full text Add to dashboard Cite

A worldwide accident survey of manned and unmanned aircraft shows in-flight loss of control remains a major contributor to aircraft accidents. Operation outside the normal fight envelope is usually subject to failure of components, inappropriate crew response, and environmental conditions. The aerodynamic model of aircraft associated with hazardous weather and abnormal conditions is inherently nonlinear and unsteady. A novel adaptive nonlinear model predictive controller is proposed and conceptually proven to ensure safe control of the Meridian unmanned aerial system in off-nominal conditions. Nonlinear model predictive controllers are capable of handling input and output constraints that directly satisfy safety and performance requirements in off-nominal conditions. The performance of a nonlinear model predictive controller relies heavily on the physics-based model. Controller performance is improved by updating the physicsbased model by using real-time nonlinear estimation of aerodynamic forces. Real-time nonlinear estimation of aerodynamic forces is obtained utilizing artificial neural networks with time-varying adaptive parameters. The adaptive nonlinear model predictive controller, coupled with real-time parameter identification, is shown to exhibit robust characteristics and to successfully mitigate the impact of nonlinear and unsteady aerodynamics while preventing loss of control.

show abstract

Prediction of aerodynamic characteristics of an aircraft model with and without winglet using fuzzy logic technique

Cited by 29 publications

References 12 publications

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Parametric optimization for the production of nanostructure in high carbon steel chips via machining

Development of fuzzy logic model to predict the engine performance of fish oil biodiesel with diethyl ether

Online artificial neural network model‐based nonlinear model predictive controller for the meridian UAS

Contact Info

Product

Resources

About