Applying systematic design: the flight refuelling probe project

This report is a product of close industry-academia collaboration between British Aerospace and the Cambridge Engineering Design Centre (EDC). British Aerospace designs and integrates some of the most complex systems in the world, and its expertise in this field has enabled the company to become the United Kingdom's largest exporter. However, to stay at the forefront of the highly competitive aerospace industry it is necessary to seek new ways to work more effectively and more efficiently. The Cambridge EDC has played a part in supporting these needs by providing access to the methods and tools that it has developed for improving the process of designing mechanical systems. The EDC has gained an international reputation for the quality of its work in this subject. Thus, the collaboration is between two organisations each of whom are leaders in their respective fields. The central aim of the project has been to demonstrate how a systematic design process can be applied to a real design task identified by industry. The task selected was the design of a flight refuelling probe which would enable a combat aircraft to refuel from a "flying tanker". However, the systematic approach, methods and tools described in this report are applicable to most engineering design tasks. The findings presented in this report provide a sound basis for comparing the recommended systematic design process with industrial practice. The results of this comparison would enable the company to define ways in which its existing design process can be improved. This research project has a high degree of industrial relevance. The value of the work may be judged in terms of the opportunities it opens up for positive changes to the company's engineering operations. Several members of the EDC have contributed to the project. These include Dr Lucienne Blessing, Dr Stuart Burgess, Dr Amaresh Chakrabarti, Major Mark Nowack, Aylmer Johnson and Dr Paul Weaver. At British Aerospace special thanks must go to Alan Dean and David Halliday for their interest and the support they have given. The project has been managed by Dr Nigel Upton of British Aerospace during a 3 year secondment to the EDC.

Supporting excavations in clay - From analysis to decision-making

Finite Element Analysis (FEA) is used to calibrate a decision-making tool based on an extension of the Mobilized Strength Design (MSD) method which permits the designer an extremely simple method of predicting ground displacements during construction. This newly extended MSD approach accommodates a number of issues which are important in underground construction between in-situ walls, including: alternative base heave mechanisms suitable either for wide excavations in relatively shallow soft clay strata, or narrow excavations in relatively deep soft strata; the influence of support system stiffness in relation to the sequence of propping of the wall; and the capability of dealing with stratified ground. These developments should make it possible for a design engineer to take informed decisions on the relationship between prop spacing and ground movements, or the influence of wall stiffness, or on the need for and influence of a jet-grouted base slab, for example, without having to conduct project-specific FEA. © 2009 Taylor & Francis Group.

Adaptation to stable and unstable dynamics achieved by combined impedance control and inverse dynamics model.

This study compared adaptation in novel force fields where trajectories were initially either stable or unstable to elucidate the processes of learning novel skills and adapting to new environments. Subjects learned to move in a null force field (NF), which was unexpectedly changed either to a velocity-dependent force field (VF), which resulted in perturbed but stable hand trajectories, or a position-dependent divergent force field (DF), which resulted in unstable trajectories. With practice, subjects learned to compensate for the perturbations produced by both force fields. Adaptation was characterized by an initial increase in the activation of all muscles followed by a gradual reduction. The time course of the increase in activation was correlated with a reduction in hand-path error for the DF but not for the VF. Adaptation to the VF could have been achieved solely by formation of an inverse dynamics model and adaptation to the DF solely by impedance control. However, indices of learning, such as hand-path error, joint torque, and electromyographic activation and deactivation suggest that the CNS combined these processes during adaptation to both force fields. Our results suggest that during the early phase of learning there is an increase in endpoint stiffness that serves to reduce hand-path error and provides additional stability, regardless of whether the dynamics are stable or unstable. We suggest that the motor control system utilizes an inverse dynamics model to learn the mean dynamics and an impedance controller to assist in the formation of the inverse dynamics model and to generate needed stability.

Impedance control is tuned to multiple directions of movement

Humans are able to learn tool-handling tasks, such as carving, demonstrating their competency to make and vary the direction of movements in unstable environments. It has been shown that when a single reaching movement is repeated in unstable dynamics, the central nervous system (CNS) learns an impedance internal model to compensate for the environment instability. However, there is still no explanation for how humans can learn to move in various directions in such environments. In this study, we investigated whether and how humans compensate for instability while learning two different reaching movements simultaneously. Results show that when performing movements in two different directions, separated by a 35° angle, the CNS was able to compensate for the unstable dynamics. After adaptation, the force was found to be similar to the free movement condition, but stiffness increased in the direction of instability, specifically for each direction of movement. Our findings suggest that the CNS either learned an internal model generalizing over different movements, or alternatively that it was able to switch between specific models acquired simultaneously. © 2008 IEEE.

Impedance control is selectively tuned to multiple directions of movement

Humans are able to learn tool-handling tasks, such as carving, demonstrating their competency to make movements in unstable environments with varied directions. When faced with a single direction of instability, humans learn to selectively co-contract their arm muscles tuning the mechanical stiffness of the limb end point to stabilize movements. This study examines, for the first time, subjects simultaneously adapting to two distinct directions of instability, a situation that may typically occur when using tools. Subjects learned to perform reaching movements in two directions, each of which had lateral instability requiring control of impedance. The subjects were able to adapt to these unstable interactions and switch between movements in the two directions; they did so by learning to selectively control the end-point stiffness counteracting the environmental instability without superfluous stiffness in other directions. This finding demonstrates that the central nervous system can simultaneously tune the mechanical impedance of the limbs to multiple movements by learning movement-specific solutions. Furthermore, it suggests that the impedance controller learns as a function of the state of the arm rather than a general strategy. © 2011 the American Physiological Society.

Generalization in Adaptation to Stable and Unstable Dynamics

Humans skillfully manipulate objects and tools despite the inherent instability. In order to succeed at these tasks, the sensorimotor control system must build an internal representation of both the force and mechanical impedance. As it is not practical to either learn or store motor commands for every possible future action, the sensorimotor control system generalizes a control strategy for a range of movements based on learning performed over a set of movements. Here, we introduce a computational model for this learning and generalization, which specifies how to learn feedforward muscle activity in a function of the state space. Specifically, by incorporating co-activation as a function of error into the feedback command, we are able to derive an algorithm from a gradient descent minimization of motion error and effort, subject to maintaining a stability margin. This algorithm can be used to learn to coordinate any of a variety of motor primitives such as force fields, muscle synergies, physical models or artificial neural networks. This model for human learning and generalization is able to adapt to both stable and unstable dynamics, and provides a controller for generating efficient adaptive motor behavior in robots. Simulation results exhibit predictions consistent with all experiments on learning of novel dynamics requiring adaptation of force and impedance, and enable us to re-examine some of the previous interpretations of experiments on generalization. © 2012 Kadiallah et al.

Making the deployment of pico-PV more sustainable along the value chain

Pico-PV is an excellent technology for bringing electric light to rural areas in the developing world and replacing kerosene lanterns and candles. However, as pico-PV is a comparatively new technology, relatively little is known about appropriate methods for sustainable product development and deployment. For this reason current dissemination methods are often ineffective and unsustainable. This research aims to help project developers deploy pico-PV technologies successfully and in a sustainable manner. To achieve this, a conceptual framework of key sustainability criteria along the value chain was developed and tested. The analysis revealed that the most important criteria for the sustainable deployment of pico-PV systems are: (a) easy and safe operation of the product; (b) that a system for product return is established; (c) the retailer understands the target market and (d) the end-user is aware of the product's existence and its benefits. This research reveals that criteria (b) and (c) are of greatest concern. In light of these findings, the authors propose to focus on the following five factors; namely: (a) raising awareness for certification and creating market reassurance; (b) introducing support mechanisms to facilitate local repair; (c) using existing supply channels and establishing in-country (dis)assembly; (d) introducing financial support mechanisms at product supply stages and; (e) undertaking marketing campaigns. © 2013 Elsevier Ltd. All rights reserved.