METHODOLOGICAL BASES AND PRACTICAL ASPECTS OF OPTIMIZATION TASKS OF THE BEARING STRUCTURES OF THE STRAPDOWN INERTIAL NAVIGATION SYSTEMS

Abstract

This article describes approaches to solving problems of optimization of bearing structure of strapdown inertial navigation systems (SINS). A typical optimization problem in this case is multiobjective parametric optimization of the bearing structure of the SINS accelerometer triad in order to minimize the mass of the bearing structure and minimize deviation angles of the accelerometer axes under the action of external loads. The ANSYS Mechanical and ANSYS DesignXplorer modules are used as a tool for numerical modeling and optimization, respectively. Practical issues related to parameterization of SINS bearing structure 3D-models, calculation of accelerometer axes deviation angles, possible variants of numerical experiment plans, estimation of response sensitivity to input parameters, generation and refinement of the response surface, and multiobjective optimization are considered. For the rational parametrization of geometry, the SINS device assembly was decomposed, as a result of which the parts and structural elements that have the greatest influence on the considered objective functions were identified. To calculate the deviation angles of the sensitive elements axes, special two-node finite elements and relations for the Bryant angles were used, which describe the relative position in space of two coordinate systems. When planning a numerical experiment, at the first stage of optimization, a central composition plan was used, and at subsequent stages, the parameter space was filled using the Latin hypercube method with the option of relations between parameters, which made it possible to avoid degenerate design options. The response surface was built using the genetic aggregation method and subsequently refined based on a set of optimal solutions. Optimization for conflicting goals of mass minimization and stiffness maximization was carried out using a multiobjective genetic algorithm. The described set of approaches to solving optimization problems as a result of an exemplary series of calculations made it possible to reduce the mass of a serial SINS bearing structure part by 23% with fixed stiffness.