The technical potential for reducing metal requirements through lightweight product design

Metal production consumes around 10% of all global energy, so is a significant driver of climate change and other concerns about sustainability. Demand for metal is rising and forecast to double by 2050 through a combination of growing total demand from developing countries, and ongoing replacement demand in developed economies. Metal production is already extremely efficient, so the major opportunities for emissions abatement in the sector are likely to arise from material efficiency - using less new metal to meet demand for services. Therefore this paper examines the opportunity to reduce requirements for steel and aluminium by lightweight design. A set of general principles for lightweight design are proposed by way of a simple analytical example, and are then applied to five case study products which cumulatively account for 30% of global steel product output. It is shown that exploiting lightweight design opportunities for these five products alone could reduce global steel requirements by 5%, and similar savings in aluminium products could reduce global aluminium requirements by 7%. If similar savings to those in the design case studies were possible in all steel and aluminium products, total material requirements could be reduced by 25-30%. However, many of these light-weighting measures are, at present, economically unattractive, and may take many years to implement. © 2011 Elsevier B.V. All rights reserved.

Analysis of document-mining techniques and tools for technology intelligence: Discovering knowledge from technical documents

This research proposes a method for extracting technology intelligence (TI) systematically from a large set of document data. To do this, the internal and external sources in the form of documents, which might be valuable for TI, are first identified. Then the existing techniques and software systems applicable to document analysis are examined. Finally, based on the reviews, a document-mining framework designed for TI is suggested and guidelines for software selection are proposed. The research output is expected to support intelligence operatives in finding suitable techniques and software systems for getting value from document-mining and thus facilitate effective knowledge management. Copyright © 2012 Inderscience Enterprises Ltd.

Change propagation analysis in complex technical systems

Understanding how and why changes propagate during engineering design is critical because most products and systems emerge from predecessors and not through clean sheet design. This paper applies change propagation analysis methods and extends prior reasoning through examination of a large data set from industry including 41,500 change requests, spanning 8 years during the design of a complex sensor system. Different methods are used to analyze the data and the results are compared to each other and evaluated in the context of previous findings. In particular the networks of connected parent, child and sibling changes are resolved over time and mapped to 46 subsystem areas. A normalized change propagation index (CPI) is then developed, showing the relative strength of each area on the absorber-multiplier spectrum between -1 and +1. Multipliers send out more changes than they receive and are good candidates for more focused change management. Another interesting finding is the quantitative confirmation of the "ripple" change pattern. Unlike the earlier prediction, however, it was found that the peak of cyclical change activity occurred late in the program driven by systems integration and functional testing. Patterns emerged from the data and offer clear implications for technical change management approaches in system design. Copyright © 2007 by ASME.

Bayesian non-parametrics and the probabilistic approach to modelling.

Modelling is fundamental to many fields of science and engineering. A model can be thought of as a representation of possible data one could predict from a system. The probabilistic approach to modelling uses probability theory to express all aspects of uncertainty in the model. The probabilistic approach is synonymous with Bayesian modelling, which simply uses the rules of probability theory in order to make predictions, compare alternative models, and learn model parameters and structure from data. This simple and elegant framework is most powerful when coupled with flexible probabilistic models. Flexibility is achieved through the use of Bayesian non-parametrics. This article provides an overview of probabilistic modelling and an accessible survey of some of the main tools in Bayesian non-parametrics. The survey covers the use of Bayesian non-parametrics for modelling unknown functions, density estimation, clustering, time-series modelling, and representing sparsity, hierarchies, and covariance structure. More specifically, it gives brief non-technical overviews of Gaussian processes, Dirichlet processes, infinite hidden Markov models, Indian buffet processes, Kingman's coalescent, Dirichlet diffusion trees and Wishart processes.

Impact of lubricant composition on low-speed pre-ignition

One of the limits on the maximum fuel efficiency benefit to be gained from turbocharged, downsized gasoline engines is the occurrence of pre-ignitions at low engine speed. These pre-ignitions may lead to high pressures and extreme knock (megaknock or superknock) which can cause severe engine damage. Though the mechanism leading to megaknock is not completely resolved, pre-ignitions are thought to arise from local autoignition of areas in the cylinder which are rich in low ignition delay "contaminants" such as engine oil and/or heavy ends of gasoline. These contaminants are introduced to the combustion chamber at various points in the engine cycle (e.g. entering from the top land crevice during blow-down or washed from the cylinder walls during DI wall impingement). This paper presents results from tests in which model "contaminants", consisting of engine lubricant base stocks, base stocks mixed with fuel and base stocks mixed with one or more additives were injected directly into a test engine to determine their propensity to ignite. The ignition tendency was found to be lower for less reactive base stocks and for base stocks mixed with certain additives. Further, when small amounts of fuel were mixed with relatively non-ignitive lubricant base stocks the ignition tendency was found to increase significantly. These results may guide development of new lubricants which could be used to reduce megaknock in downsized engines. Copyright © 2014 SAE International.