Computational grid compacting for testing computing and control program modules of flight mission built on the input data priority basis


DOI: 10.34759/trd-2020-111-15

Аuthors

Lyapin A. A.

Company State Rocket Centre Academician V.P.Makeyev, 1, Turgoyakskoye shosse, Miass, Chelyabinsk Region, 456300, Russia

e-mail: lyapin-sasha@mail.ru

Abstract

The article suggests the mesh-building technique algorithm improving for program modules testing as a part of the software for the flight mission computing and monitoring based on the input data priorities building.

The software support for the flight mission computing and monitoring (hereinafter referred to as SSFMCM) consists of a set of interconnected elements (program modules) [1]. A program module (further PM) is a functionally completed software implementation of a SSFMCM partial task (algorithm). Development and testing of the above said program modules may be realized by the special program tool (BTTesting) application [2-7]. Testing methodology (automated investigation testing [8-15], and calculations automation in aerospace engineering [16]) embedded in BTTesting implies a computational grid formation based on the input data priorities building of the PM being tested.

A testing methodology implemented in BTTesting (automated explorary testing [8-15], automation of analyses in aerospace industry [16]) means the generation of a computational grid on the basis of the attachment of priorities of input data of a PM under test.

The input data priorities building technique and computational grid generation algorithm were presented in [3]. The above appointed algorithm allows increase the number of testing steps for the input parameters with higher priority (the higher the perturbation of the resulting data, the higher its priority). However, the resulting data perturbations depending on the input parameter region variation is unevenly distributed. Knowledge of the above-appointed dependence will allow generation of the computational testing grid with uneven (discrete) step and, thus, increase the number of nodes in the regions with maximum deflection of the resulting data.

The article presents the algorithm for building the above mentioned dependence and discrete computational grid based on the assumption that the highlighted domains (with higher variations of the resulting data) contain maximum number of faults (incorrect decisions).

The developed algorithm allows compressing the testing grid within the input data areas variations leading to maximum deflection of the results, which allows obtaining the greater number of incorrect solutions per the same time unit, and, thus, employ the time resources allocated for testing more effectively.

Keywords:

flight mission computing and control, algorithm, block-diagram, data priorities

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