نشریه فناوری در مهندسی هوافضا
سال ششم شماره 4 (پیاپی 23، زمستان 1401)

In general, there are various analytical methods to evaluate the reliability of repairable and non-repairable systems, and in particular prediction. Reliability prediction is one of the most common forms of reliability analysis that is used to estimate and predict failure rate, reliability, availability, and mean time-to-failure of components and the whole system. Markov is a method for modeling the stochastic behavior of systems that are continuously or continuously changing over time or in space. These predictions are used to evaluate design feasibility, compare alternative designs, identify potential failure areas, compromise system design factors, and detection/ Tracing reliability improvements. In this paper, in order to predict the reliability assessment indices in the UAVs and based on the Markov analysis method, a model based on the fixed failure and repair rates has been proposed; The derived and proposed relations in this study have the ability to generalize to other types of drone.

Considering the complexity of analyzing the phenomena affecting the aerodynamic performance of the main rotor helicopter and how to simulate the dynamic movements of the blade relative to the center of rotation, Using methods such as blade element theory together with computational fluid dynamics can be a simpler and less expensive solution than the physical simulation of a helicopter rotor. The geometric characteristics of the rotor of the Bell UH-1 helicopter have been investigated as a virtual disc in two modes of the rotor alone and with the body in static and forward flight conditions. To simulate the flow, unsteady compressible equations with turbulence components are used, and for their discretization, upwind second-order accuracy is used, and the functional effects of the rotor in the form of a spring component are included in these equations.

Development of Physics-Informed Neural Networks (PINNs) as nonlinear dynamics surrogates is investigated. PINNs are unsupervised neural networks in which the input-output relationship is established via a specific dynamic relationship (differential equation). In this regard, the derivatives are determined by utilizing Automatic Differentiation over the network’s graph. Hence, PINNs can be utilized to build complex surrogates for nonlinear dynamical systems which can later be used in real-time control applications. In this study, it is shown that PINNs can adequately capture the dynamics investigated. Even in regions of the state space where there are no training sample points, a PINN surrogate provides an acceptable approximation of the dynamical system. To investigate the hypothesis, three categories of nonlinear dynamics are examined: (1) self-sustained, (2) excitatory, and (3) chaotic systems. As implied by the results, PINNs can estimate self-sustaining and chaotic systems with sufficient accuracy. However, the concept is not as successful with excitatory dynamics that mandates further detailed studies on these surrogates.

Due to the recent applications of generating strong sound waves from thermo-acoustics to acoustic cleaning, it is necessary to modify the previous equations of sound propagation, which were based on linearization assumptions and developed for ordinary applications. In this paper, the sound propagation equations in a horn are first extracted. This starts with writing the equations of momentum and mass conservation and then ends with defining the potential velocity function and placing it in the partial differential equations. By transforming this equation to the frequency domain, the problem becomes a boundary value problem. Frequency response curves are extracted by applying the boundary conditions. These equations can be solved using the Range Kutta method. Since there is an analytical response for exponential horns assuming the sound propagation is linear, the results are validated in this case.

In this study, the variation of amplitude and wavelength of the sinusoidal wing in the post-stall condition have been studied to evaluate the effect of each of these parameters on the control of the stall phenomenon and the stall’s sensitivity to Each of these two parameters is measured. For this purpose, an EPPLER considered as a wavy leading-edge wing’s cross-section and a numerical simulation is performed for Reynolds 140,000 at 22 AOA, which is exactly at post stall condition. The results show that the height of the flow separation area is more sensitive to the amplitude in tubercled wings, while the width of the flow separation area is strongly dependent on the wavelength of the sinusoidal function. The lift coefficient to drag coefficient ratio (L/D) is also more sensitive to increasing the amplitude and has decreased by 7.5%.

Nowadays in the world, due to increasing development of fabrication and design, fabrication of composite materials with variable stiffness are provided. These composites are widely used in various industries, especially in the aviation industry due to the simultaneous benefits of metal and composite. As an innovation in this research, the frequency analysis of variable stiffness fiber-metal laminated plates under thermal load are investigated using the semi-analytical finite strip method based on the classical laminated plate theory. In this regard, the effects of boundary conditions, stacking sequences, number of layers and effect of geometric dimensions on the frequency behavior of mentioned plates are studied. The results show that frequency of variable stiffness fiber metal laminated plates by increasing the temperature, thickness and boundary constraints in different boundary conditions are increased. Also, the stacking sequences and plate dimensions are affected the frequency of plates. To check the validity, some of results are compared with several different references and show a good agreement.