The dynamics of industrial emergence

The need to stimulate, identify and nurture new industries is a prominent challenge in advanced economies. While basic science represents a valuable source of new ideas and opportunities, it can often take decades before this science finally finds application in the market. While numerous studies have to date focused on aspects of industrial evolution, (e.g. innovation, internationalisation, new product introduction, technological lifecycles and emerging technologies), far fewer have focused on technology-based industrial emergence. It is clear that if assistance is to be provided to firms and industrial policymakers attempting to navigate industrial emergence then we need an improved understanding of the characteristics and dynamics of this phenomenon. Accordingly, this paper reviews published work from a range of disparate disciplines - evolutionary theory, social construction of technology (SCOT), complexity science, industrial dynamics and technology management - to identify these dynamics. Through this review we conceptualise industrial emergence as a co-evolutionary process in which nonlinear dynamics operate. Industrial emergence is sensitive to the initial availability of resources and the market applications, with growth dependent on the supply-demand coupling, agents' actions to reduce uncertainty and catalytic events. Through synthesizing these key dynamics we go on to propose a conceptual model for industrial emergence. © 2010 IEEE.

Experimental study into the flow physics of three-dimensional shock control bumps

This paper describes a fundamental experimental study of the flow structure around a single three-dimensional (3D) transonic shock control bump (SCB) mounted on a flat surface in a wind tunnel. Tests have been carried out with a Mach 1.3 normal shock wave located at a number of streamwise positions relative to the SCB. Details of the flow have been studied using the experimental techniques of schlieren photography, surface oil flow visualization, pressure sensitive paint, and laser Doppler anemometry. The results of the work build on the findings of previous researchers and shed new light on the flow physics of 3D SCBs. It is found that spanwise pressure gradients across the SCB ramp and the shape of the SCB sides affect the magnitude and uniformity of flow turning generated by the bump, which can impact on the spanwise propagation of the quasi-two-dimensional (2D) shock structure produced by a 3DSCB. At the bump crest, vortices can form if the pressure on the crest is significantly lower than at either side of the bump. The trajectories of these vortices, which are relatively weak, are strongly influenced by any spanwise pressure gradients across the bump tail. Asignificant difference between 2D and 3D SCBs highlighted by the study is the impact of spanwise pressure gradients on 3D SCB performance. The magnitude of these spanwise pressure gradients is determined largely by SCB geometry and shock position. Copyright © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

ℓasso MPC: Smart regulation of over-actuated systems

In this paper, a novel MPC strategy is proposed, and referred to as asso MPC. The new paradigm features an 1-regularised least squares loss function, in which the control error variance competes with the sum of input channels magnitude (or slew rate) over the whole horizon length. This cost choice is motivated by the successful development of LASSO theory in signal processing and machine learning. In the latter fields, sum-of-norms regularisation have shown a strong capability to provide robust and sparse solutions for system identification and feature selection. In this paper, a discrete-time dual-mode asso MPC is formulated, and its stability is proven by application of standard MPC arguments. The controller is then tested for the problem of ship course keeping and roll reduction with rudder and fins, in a directional stochastic sea. Simulations show the asso MPC to inherit positive features from its corresponding regressor: extreme reduction of decision variables' magnitude, namely, actuators' magnitude (or variations), with a finite energy error, being particularly promising for over-actuated systems. © 2012 AACC American Automatic Control Council).