Search results

The purpose of this paper is to present the application of the neural-based estimation method, Neural-Gauss-Newton (NGN), using the real flight data of a small unmanned aerial vehicle (UAV).

The UAVs in general are lighter in weight and their flight is usually influenced by the atmospheric winds because of their relatively lower cruise speeds. During the presence of the atmospheric winds, the aerodynamic forces and moments get modified significantly and the accurate mathematical modelling of the same is highly challenging. This modelling inaccuracy during parameter estimation is routinely treated as the process noise. Furthermore, because of the limited dimensions of the small UAVs, the measurements are usually influenced by the disturbances caused by other subsystems. To handle these measurement and process noises, the estimation methods based on neural networks have been found reliable in the manned aircrafts.

Six sets of compatible longitudinal flight data of the designed UAV have been chosen to estimate the parameters using the NGN method. The consistency in the estimates is verified from the obtained mean and the standard deviation and the same has been validated by the proof-of-match exercise. It is evident from the results that the NGN method was able to perform on a par with the conventional maximum likelihood method.

This is a partial outcome of the research carried out in estimating parameters from the UAVs.

In The Great Derangement, the Indian writer Amitav Ghosh examines the present inability to understand and represent the scale and violence of the environmental crisis. The book is a passionate awakening call for collective action to drive change, with Ghosh clearly identifying the limits of the present framework of values, which inhibits politicians, industrialists and economists from moving towards a truly sustainable civilization. In the Anthropocene, non-human and post-human factors are raising questions about the concept of a silent Nature that can be domesticated for human advantage and the perspective of continuous progress – both of which have dominated the modern age. Nevertheless, the detailed scientific analysis of the violation of the planet’s limited capacities continues to be refuted, triggering irrational, short-term utilitarian behaviours which are preventing the fundamental changes required for the transition to sustainable development. Artists, philosophers and writers can play an invaluable role in reframing our ways of thinking, filling the gap between scientific knowledge and emotional perception. Pioneering artistic experiments are appearing all over the world, from both well-established and emerging artists, and through collective processes, and this cultural movement is setting the scene for a new wave of eco-entrepreneurs driven by the altruistic mission of saving the planet. As has happened in many previous crises, it is again in the hands of artists to redefine how we perceive ourselves and so to support the emergence of new ideas, new learning, and finally to shape society and the economy around a renewed sense of the future for humankind on Earth.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

The purpose of this paper is to evaluate the accuracy of linear and quasi-steady aerodynamic models of aircraft aerodynamic models when a small unmanned aerial system flies in the presence of strong wind and gust at a high angle of attack and a high sideslip angle.

Compatibility analysis were done to improve the quality of recorded flight test data. A robust method called fuzzy logic modeling is used to set up the aerodynamic models. The reduced frequency is used to represent the unsteadiness of the flow field according to Theodorsen’s theory. The work done by the aerodynamic moments on the motions is used as the criteria of stability.

In portions of flight, aircraft’s stability and control derivatives were unstable and nonlinear functions of airflow angles and angular rates. The roll angle had an important effect on unsteadiness of directional oscillatory damping derivatives. The pilot-induced oscillation and wing rock possibilities were investigated and dismissed so that the lateral directional oscillatory motion was classified as a nonlinear Dutch roll oscillation. Major modeling enhancements or real-time parameter identification are required for the control of a small unmanned aerial system in off-nominal conditions. The robustness tests of all-weather autopilot systems must be done with consideration of sign change.

Oscillatory damping derivatives were reconstructed using flight test data and the inadequacy of engineering level software in predicting this type of instability observed and demonstrated for a flight in the presence of wind shear and external disturbances.

Natural convection in a square cavity with a centrally located partition is considered. While one of the side walls is fully active, the other is partly insulated. Numerical simulation, based on the finite element method, has been carried out for different lengths of the active surface. The results have been compared with the cases when the cavity is without partition as well as the case of a partitioned cavity with fully active side walls.

The purpose of this study is to estimate aerodynamic parameters using regularized regression-based methods.

Regularized regression methods used are LASSO, ridge and elastic net.

A viable option of aerodynamic parameter estimation from regularized regression-based methods is found.

Efficacy of the methods is examined on flight test data.

This study provides regularized regression-based methods for aerodynamic parameter estimation from the flight test data.

Here an attempt has been made to study the MHD flow of a dusty, electrically conducting, visco‐elastic Rivlin‐Ericksen fluid starting from the rest with time‐dependent types applied at the free surface. The analytical expression for velocity profiles of the fluid and dust particles have been found by using the Laplace transform technique. Finally the effects of magnetic field time, elastic parameter and mass concentration of dust particles are discussed with the help of graphs and tables.

This paper aims to investigate the thermal performance of equivalent square and circular thermal systems and compare the heat transport and irreversibility of magnetohydrodynamic (MHD) nanofluid flow within these systems.

The research uses a constraint-based approach to analyze the impact of geometric shapes on heat transfer and irreversibility. Two equivalent systems, a square cavity and a circular cavity, are examined, considering identical heating/cooling lengths and fluid flow volume. The analysis includes parameters such as magnetic field strength, nanoparticle concentration and accompanying irreversibility.

This study reveals that circular geometry outperforms square geometry in terms of heat flow, fluid flow and heat transfer. The equivalent circular thermal system is more efficient, with heat transfer enhancements of approximately 17.7%. The corresponding irreversibility production rate is also higher, which is up to 17.6%. The total irreversibility production increases with Ra and decreases with a rise in Ha. However, the effect of magnetic field orientation (γ) on total EG is minor.

Further research can explore additional geometric shapes, orientations and boundary conditions to expand the understanding of thermal performance in different configurations. Experimental validation can also complement the numerical analysis presented in this study.

This research introduces a constraint-based approach for evaluating heat transport and irreversibility in MHD nanofluid flow within square and circular thermal systems. The comparison of equivalent geometries and the consideration of constraint-based analysis contribute to the originality and value of this work. The findings provide insights for designing optimal thermal systems and advancing MHD nanofluid flow control mechanisms, offering potential for improved efficiency in various applications.

The purpose of this paper is to propose a new aerodynamic parameter estimation methodology based on neural network and output error method, while the output error method is improved based on particle swarm algorithm.

Firstly, the algorithm approximates the dynamic characteristics of aircraft based on feedforward neural network. Neural network is trained by extreme learning machine, and the trained network can predict the aircraft response at (k + 1)th instant given the measured flight data at kth instant. Secondly, particle swarm optimization is used to enhance the convergence of Levenberg–Marquardt (LM) algorithm, and the improved LM method is used to substitute for the Gauss Newton algorithm in output error method. Finally, the trained neural network is combined with the improved output error method to estimate aerodynamic derivatives.

Neither depending on the initial guess of the parameters to be estimated nor requiring numerical integration of the aircraft motion equation, the proposed algorithm can be used for unstable aircraft and is successfully applied to extract aerodynamic derivatives from both simulated and real flight data.

The proposed method requires iterative calculation and can only identify parameters offline.

The proposed method is successfully applied to estimate aircraft aerodynamic parameters and can also be used as a new algorithm for other optimization problems.

In this study, the output error method is improved to reduce the dependence on the initial value of parameters and expand its application scope. It is applied in aircraft aerodynamic parameter identification together with neural network.