Understanding and navigating industrial emergence

Industrial emergence is a broad and complex domain, with relevant perspectives ranging in scale from the individual entrepreneur and firm with the business decisions and actions they make to the policies of nations and global patterns of industrialisation. The research described in this article has adopted a holistic approach, based on structured mapping methods, in an attempt to depict and understand the dynamics and patterns of industrial emergence across a broad spectrum from early scientific discovery to large-scale industrialisation. The breadth of scope and application has enabled a framework and set of four tools to be developed that have wide applicability. The utility of the approaches has been demonstrated through case studies and trials in a diverse range of industrial contexts. The adoption of such a broad scope also presents substantial challenges and limitations, with these providing an opportunity for further research. © IMechE 2013.

An exploration of risk management in global industrial investment

This article explores risk management in global industrial investment by identifying linkages and gaps between theories and practices. It identifies opportunities for further development of the field. Three related bodies of literature have been reviewed: risk management, global manufacturing and investment. The review suggests that risk management in global manufacturing is overlooked in the literature; that existing theoretical risk management processes are not well developed in the global manufacturing context and that the investment literature applies mainly to financial risk assessment rather than investment risk management structures. Further, there appears to be a serious lack of systematic industrial risk management in investment decision making. This article highlights the opportunities to deploy current good practices more effectively as well as the need to develop more robust theories of industrial investment risk management. The approach adopted to investigate this multidisciplinary topic included a historical review of literature to understand the diverse background of theoretical development. A case study research approach was adopted to collect data, involving four global manufacturing companies and one risk management advisory company to observe the patterns and rationale of current practices. Supporting arguments from secondary data sources reinforced the findings. The research focuses risk management in global industrial investment. It links theories with practice to understand the existing knowledge gap and proposes key research themes for further research. © 2013 Macmillan Publishers Ltd. 1460-3799 Risk Management.

Adapting data processing to compare model and experiment accurately: A discrete element model and magnetic resonance measurements of a 3d cylindrical fluidized bed

Discrete element modeling is being used increasingly to simulate flow in fluidized beds. These models require complex measurement techniques to provide validation for the approximations inherent in the model. This paper introduces the idea of modeling the experiment to ensure that the validation is accurate. Specifically, a 3D, cylindrical gas-fluidized bed was simulated using a discrete element model (DEM) for particle motion coupled with computational fluid dynamics (CFD) to describe the flow of gas. The results for time-averaged, axial velocity during bubbling fluidization were compared with those from magnetic resonance (MR) experiments made on the bed. The DEM-CFD data were postprocessed with various methods to produce time-averaged velocity maps for comparison with the MR results, including a method which closely matched the pulse sequence and data processing procedure used in the MR experiments. The DEM-CFD results processed with the MR-type time-averaging closely matched experimental MR results, validating the DEM-CFD model. Analysis of different averaging procedures confirmed that MR time-averages of dynamic systems correspond to particle-weighted averaging, rather than frame-weighted averaging, and also demonstrated that the use of Gaussian slices in MR imaging of dynamic systems is valid. © 2013 American Chemical Society.

The industrial emergence of commercial inkjet printing

Purpose: The purpose of this paper is to investigate how supply and demand interact during industrial emergence. Design/methodology/approach: The paper builds on previous theorising about co-evolutionary dynamics, exploring the interaction between supply and demand in a study of the industrial emergence of the commercial inkjet cluster in Cambridge, UK. Data are collected through 13 interviews with professionals working in the industry. Findings: The paper shows that as new industries emerge, asynchronies between technology supply and market demand create opportunities for entrepreneurial activity. In attempting to match innovative technologies to particular applications, entrepreneurs adapt to the system conditions and shape the environment to their own advantage. Firms that successfully operate in emerging industries demonstrate the functionality of new technologies, reducing uncertainty and increasing customer receptiveness. Research limitations/implications: The research is geographically bounded to the Cambridge commercial inkjet cluster. Further studies could consider commercial inkjet from a global perspective or test the applicability of the findings in other industries. Practical implications: Technology-based firms are often innovating during periods of industrial emergence. The insights developed in this paper help such firms recognise the emerging context in which they operate and the challenges that need to overcome. Originality/value: As an in depth study of a single industry, this research responds to calls for studies into industrial emergence, providing insights into how supply and demand interact during this phase of the industry lifecycle. © Emerald Group Publishing Limited.

Influence of turbulence-chemistry interaction for n-heptane spray combustion under diesel engine conditions with emphasis on soot formation and oxidation

The influence of the turbulence-chemistry interaction (TCI) for n-heptane sprays under diesel engine conditions has been investigated by means of computational fluid dynamics (CFD) simulations. The conditional moment closure approach, which has been previously validated thoroughly for such flows, and the homogeneous reactor (i.e. no turbulent combustion model) approach have been compared, in view of the recent resurgence of the latter approaches for diesel engine CFD. Experimental data available from a constant-volume combustion chamber have been used for model validation purposes for a broad range of conditions including variations in ambient oxygen (8-21% by vol.), ambient temperature (900 and 1000 K) and ambient density (14.8 and 30 kg/m3). The results from both numerical approaches have been compared to the experimental values of ignition delay (ID), flame lift-off length (LOL), and soot volume fraction distributions. TCI was found to have a weak influence on ignition delay for the conditions simulated, attributed to the low values of the scalar dissipation relative to the critical value above which auto-ignition does not occur. In contrast, the flame LOL was considerably affected, in particular at low oxygen concentrations. Quasi-steady soot formation was similar; however, pronounced differences in soot oxidation behaviour are reported. The differences were further emphasised for a case with short injection duration: in such conditions, TCI was found to play a major role concerning the soot oxidation behaviour because of the importance of soot-oxidiser structure in mixture fraction space. Neglecting TCI leads to a strong over-estimation of soot oxidation after the end of injection. The results suggest that for some engines, and for some phenomena, the neglect of turbulent fluctuations may lead to predictions of acceptable engineering accuracy, but that a proper turbulent combustion model is needed for more reliable results. © 2014 Taylor & Francis.