نشریه فناوری در مهندسی هوافضا
سال هشتم شماره 2 (پیاپی 29، تابستان 1403)

One of the main propositions of predictive maintenance is Prognostics and Health Management (PHM) which plays a unique role in identifying, diagnosing, and predicting the health status of physical assets. To that end, one of the fundamental solutions is to assess the condition of the equipment in the aviation sector in order to provide maintenance plans by determining the trend of deterioration or destruction. In this study, a developed model of an artificial neural network was presented, focusing on the concept of deep learning and its comparison with other conventional methods, in response to the limitations and uncertainties in traditional prediction methods in determining the deterioration process of the equipment. The comparative results revealed that the deep learning neural network method with a prediction accuracy of 94% had a high performance in determining the deterioration process in aircraft turbine engines compared to other conventional methods. The findings of this study can be used to predict the remaining useful life of aviation industry equipment and to provide appropriate maintenance programs.

This research investigates the effect of fence on wingtip vortices and control surfaces in a bird-like aircraft using numerical methods. In designing and placing the fence, average dimensions extracted from wing root vortices at different angles of attack have been used. In addition, the fence with specified dimensions have been installed at three heights and three different positions along the wing (the length of the winglet is equal to the average length of the vortex in that part, and the height of the winglet is 30% of the diameter of the vortex in that part) and have been examined at angles of attack ranging from 7 to 16 degrees. The next stage of the study is the optimal design of the dimensions of the control surfaces using a single objective optimization method. The aim of this research is to achieve the best possible design that converges to an optimal solution with minimum time and cost. The design of control surfaces is carried out at three points based on the dimensions of the wing root vortex using numerical methods. However, Computational Fluid Dynamics (CFD) analysis requires a lot of computational time.

This article describes the process of designing and simulating the performance of the predictive controller of the optimal model, which was created in order to guide the missile in a two-dimensional problem of minimizing the collision time and the distance from the target. In the design of the optimal model predictive controller, first the widely used type of implicit linear predictive model is designed and then the nonlinear predictive model is presented and their performance in missile guidance is evaluated. According to the simulations, it can be shown that the predictive controller of the implicit linear model (on-line) and the predictive controller of the optimal non-linear model control the distance of the missile's final collision with the target and the collision time well. The innovation of this article is the design of the predictive controller of the optimal model using genetic algorithm and particle community in the integrated guidance and control model.

In this research, the impact strength of functionally graded epoxy/graphene nanocomposite have been investigated. First, the samples were made for uniform and functionally graded distributions. Then, the distribution of nanoparticles has been investigated using a scanning electron microscope. Based on this investigation for uniform distribution up to 1.5% by weight of graphene nanoparticles, no signs of agglomerated particles were found. In addition, imaging was also done for the functionally graded sample and it was observed that the boundary of the layers is completely connected. Then the samples were subjected to Izod impact test and it was observed that for uniform distribution, the amount of energy absorption increased up to 1% by weight of graphene nanoparticles and then for the sample with 1.5% by weight of graphene nanoparticles, its value started to reduces. To validate the accuracy of the obtained results, modeling has been done for the mentioned samples using the finite element method and the obtained results have been compared with the experimental results.

In order to perform attitude control ground experiments, it is crucial to ensure the center of mass of the platform is coincident with the center of rotation as accurate as possible. In this paper, a linearized dynamic model of the imbalanced attitude platform is derived and a linear controller is methodically designed to stabilize the system by mass relocation. The stability conditions of the system under the proposed controller are derived. The balancing procedure starts with a parameter estimation method to estimate the center of mass offsets. Next, the PD controller is applied to align the platform’s horizontal plane with the local horizontal level. Finally, the imbalance in vertical axis can be estimated and compensated to complete the automatic mass balancing. Actuator limitations and nonlinear equations of motion are implemented in numerical simulations and the results demonstrate the effectiveness of the proposed method in significantly reducing the residual imbalance torque on the simulated platform.

: In the contemporary landscape, the utilization of multi-rotor drone technology has witnessed a notable upsurge across diverse academic, industrial, and commercial domains. These multifaceted systems have assumed pivotal roles encompassing surveillance, wireless network coverage, remote sensing, search and rescue operations, efficient package delivery, tele-medicine facilitation, security enforcement, monitoring, precision agriculture, traffic management, and infrastructure inspection, among others. The trajectory of technological advancement underscores the impending significance of intelligent quad-copters as a transformative force within the industry, promising to forge novel avenues and redefine conventional paradigms. In effect, this paradigm shift is poised to mitigate operational risks and financial outlays, thereby fostering heightened assimilation of these technological innovations. Projections underscore that drones' market penetration within the civil infrastructure sector is slated to culminate in an approximately $45 billion valuation by end of this decade. This discourse delves into an all-encompassing exploration of the manifold applications and associated challenges intrinsic to multi-rotor systems, with a particular emphasis on quad-copters, across research arenas and diverse industries. Furthermore, the discourse offers an incisive analysis of prevailing trends while shedding light on the contours of forthcoming developments in the utilization of these transformative devices.