The management of intelligence-assisted finite element analysis technology

Artificial Intelligence (AI) approaches to Finite Element Analysis (FEA), have had tentative degrees of success over the last few years and some authors have argued that effective FEA can help in the manufacture reliability and safety aspects of engineered artefacts. The author of this paper reviews how such AI techniques have been applied and in this light, the author then uses a Fuzzy Cognitive Mapping (FCM), to develop a framework for the management of intelligence-assisted FEA

Comparative evaluation of approaches in T.4.1-4.3 and working definition of adaptive module

The goal of this deliverable is two-fold: (1) to present and compare different approaches towards learning and encoding movements us- ing dynamical systems that have been developed by the AMARSi partners (in the past during the first 6 months of the project), and (2) to analyze their suitability to be used as adaptive modules, i.e. as building blocks for the complete architecture that will be devel- oped in the project. The document presents a total of eight approaches, in two groups: modules for discrete movements (i.e. with a clear goal where the movement stops) and for rhythmic movements (i.e. which exhibit periodicity). The basic formulation of each approach is presented together with some illustrative simulation results. Key character- istics such as the type of dynamical behavior, learning algorithm, generalization properties, stability analysis are then discussed for each approach. We then make a comparative analysis of the different approaches by comparing these characteristics and discussing their suitability for the AMARSi project

StrathSat-R : Deploying inflatable CubeSat structures in micro gravity

This paper presents the concepts, objectives and design of a student-led sounding rocket experiment which shall test novel inflatable devices in space conditions. This experiment is envisaged as the first step towards developing a CubeSat programme at the University of Strathclyde, which can exploit the novel concepts developed and the technical skills gained. The experiment itself aims to test novel, student developed, inflatable space structures in micro gravity and reduced pressure conditions. It consists of three distinct sections, the ejection housing on the rocket and the two ejectable modules that are based on CubeSat architecture. Shortly before reaching apogee, the two modules are ejected from the rocket and will deploy their own inflating structure during free flight. After landing, the ejectable modules recovery will rely upon a GPS position relayed to the team from the module by Globalstar transmission and a RF beacon for tracking with the recovery helicopter. The two modules carry two different structures resulting in distinct mission objectives: The aim of FRODO is to deploy an experimental passive de-orbiting system for high altitude spacecraft which will in the future utilise solar radiation pressure for orbit removal. The aim of SAM is to serve as a technology demonstrator for the residual air deployment method of a smart bio-inspired space structure. This paper contains details about the science objectives of the mission and how they will be achieved, its experimental design and the management of the student-led project