فصلنامه مکانیک هوافضا
سال هفدهم شماره 4 (پیاپی 66، زمستان 1400)

The process of computing the true values of the direction cosine matrix (DCM) is one of the important parameters for exact navigation of vehicles. In other words, determination of the directions of the INS vectors in terms of the directions of the reference system (alignment) is one of the important parameters of a navigation system. In order to improve the performance of such systems, this procedure is done according to the inertial measurement unit (IMU) and global positioning system (GPS) data, when the vehicle is in motion. Duo to the stochastic noise and uncertainties in inertial measurement sensors, a data fusion algorithm is used to integrate the outputs of the IMU and GPS sensors. In this paper a novel variant horizon predictive model estimation algorithm is proposed to construct an integrated INS/GPS inertial navigation system. The horizon of the proposed algorithm is calculated based on the vehicle maneuvers. Several vehicular tests have been carried out to assess the long-term performance and accuracy of the proposed navigation algorithm. The results indicate that the proposed algorithm significantly enhances the overall navigation accuracy of low-cost integrated INS/GPS inertial navigation system, in comparison to the conventional Kalman filter algorithm.

Bi-stable or multi-stable composite laminate is one of the adaptive structures that keep their stable states and modify their shapes by load application. This paper investigates the out of plane displacement of bi-stable hybrid composite laminate with an AL layer on one side. Adding the AL layer is a novel approach, introduced to create an asymmetric bi-stability that leads to a better control of the structure and the results are evaluated experimentally and numerically.  In this regard, the deformation of plates in two stable states, with and without the AL layer (hybrid) coating have been compared. Also, the strain energy changes with room temperature and cooling time have been investigated. Adding an aluminum layer can create a platform for connecting piezoelectric actuators, memory alloys, and thermal actuators. If these laminates are connected to other symmetric or asymmetric laminates, it is possible to create continuity over the discontinuous borders and decrease the stress concentration. These laminates are suitable devices for continuous modification of the shape of aerospace structures like reflector antennas and flight control surfaces.

A drivers’ body is continuously under various vibrations, resulting in different physical disorders such as muscle-skeletal defects, neurogenic diseases, cardiovascular problems, gastrointestinal disorders, etc. In this paper, firstly the nonlinear dynamic equations of a seated human body in the half car model are investigated analytically and linearized by means of the Taylor series. Then the obtained equations are analyzed numerically by the state-space method and employed for the design of multivariable controllers using the canonical and similar transformations. Finally, the controller performance of the system is optimized to suppress the transferred vibrations to the human body using the Genetic Algorithm (GA). The results show the RMS of human body acceleration in both the horizontal and vertical directions, to be 2.76m/s2 and 0.27m/s2, with and without the controller, respectively. It decreases to 0.06m/s2 with an optimized controller; which indicates that the controller acts satisfactorily and efficiently mitigates the human body vibrations.

The friction stir welding method was first developed in 1991 at the British Welding Institute as a solid-state bonding method and employed for welding aluminum alloys. This method has high energy efficiency and good compatibility with the environment. In general, the frictional stir welding process is suitable for metals with a low melting point, whose melting welding is not of good quality. In this research, multi-objective optimization of the mechanical properties in the friction welding of AH12 1050 aluminum alloy has been performed experimentally using a combination of response surface methods and multi-objective particle swarm optimization. The Taguchi method has also been utilized to design experiments with two types of threaded cylindrical and simple tapered pins. Pin diameter, shoulder diameter, tool rotational speed, tool feed, and tool deviation angle are selected as the process variables. Specimen strength, toughness, and hardness are considered as objective functions. The optimization results and a prediction accuracy of more than 93%, show a good agreement between the surface prediction model and the experiments despite the complexity of the process.

In aerospace structures, due to the fluctuations of excitation sources such as the engine propulsion, there is a possibility of dynamic instability, which is a destructive phenomenon. In this paper, the dynamic stability of composite grid-stiffened cylindrical shells under a combinational loading of static and fluctuating forces has been investigated using the Dunnelly theory for thin-walled shells. Using the equivalent stiffness method, the stiffness of composite grid structures has also been calculated by the method of reinforcements impregnation. The development of a normal mode for motion equations leads to the system of Matthew-Hill equations. The Boltin method is used to determine the instability regions to solve the Matthew-Hill equations. The effect of iso-grid mesh reinforcement parameters such as the rib angle, circumferential and annular rib spacing and the rib cross section as well as the effect of cylindrical shell length to radius and thickness to radius ratios have been tested and compared. The validation of the natural frequency and dynamic stability results has been done by comparison with Abaqus software and other researchers' articles. The results show that by decreasing the angle of the helical ribs, in the shells of composite grid cylinders, the main instability frequency increases and the amplitude of the instability region decreases.

The sandwich panels are important structures for absorbing the explosion energy. Crushing and plastic deformation of the core along with the plastic bending of the faces are the main factors in absorbing the explosion energy in these structures. Structural components undergo significant permanent deformation after the explosion and its related energy absorption. In circular sandwich panels with symmetrical geometry and loading, the greatest amount of deformation occurs in the center of the back face. In this paper, the energy absorption of the structure and the deformation of circular metal sandwich panels with tubular core under explosion load have been studied analytically and experimentally. The tubes are arranged radially and symmetrically in the core constructions, which is a new configuration for the energy-absorbing sandwich panels in the literature. Experiments have been performed by making sandwich panels under free blast load in order to evaluate and validate the analytical results. The analytical solution is performed using the energy method by balancing the kinetic energy and the plastic work which is done by the different components of the sandwich panels. Maximum deflection, the amount of core crushing and the amount of energy absorbed by the whole structure and different parts of the structure are studied for different cases. There is a good agreement between analytical and experimental results.

This paper proposes a new method called the generalized incremental predictive Kalman filter (GIPKF) to increase the accuracy of the integrated GPS / INS systems when the satellite signal is not available. This method is performed in the laboratory and tested and evaluated using the prepared hardware. The equations governing the inertial navigation system are nonlinear and the linearization in the extended Kalman filter causes the linearization approximation error. The uncertainties in the measurement noises and system noises also, produce errors in the estimation. In the proposed method, the model errors such as the linearization error and the weighted noise matrices errors are assumed as the model’s filter error and are estimated and compensated using the concept of predictive filtering and the application of Kalman filter. In this paper first, the complete equations of the new proposed method and the relations required to integrate the GPS/INS system are explained. Then using the results of the experiments, the proposed method is compared to the extended Kalman filter method. The results show that the presented algorithm is more efficient since, when the GPS outage is about 30 seconds, the position error is reduced by about 50% due to the new method’s ability to predict and compensate for the model error. This method significantly improves the performance of inertial navigation systems.In this paper, the complete equations of the proposed new method and the relations required to integrate the GPS/INS system are first explained. Then, using the test results, the proposed new method is compared with extended Kalman filter method. The results show that the presented algorithm is more efficient when the receiver signals are blocked due to its ability to predict and compensate for model error. This method significantly improves the performance of the inertial navigation system and corrects its errors.

In this paper, a fairly comprehensive comparative study has been done on online identification methods for the dynamic model of an aircraft system. To this aim, first, the existing algorithms in this field are introduced. Then, some of these approaches including the recursive least square, the recursive extended least square, the recursive instrumental variable, the extended matrix, the radial basis function neural network, and the multilayer perceptron neural network are utilized to identify the aircraft model. To carry out the simulations, and train the neural networks, the linearized model and online data of the Boeing 747 aircraft controlled by a sliding mode controller on an arbitrary reference trajectory are employed. Finally, the efficiency of each of the above-mentioned methods is evaluated and compared to the other approaches. According to the obtained results of this research, the radial basis function neural network method has a significantly superior performance over the other algorithms due to dynamic noise estimation, independence from the system model, rejecting the linear model of the system, and higher accuracy while maintaining the appropriate speed.