Understanding luxury in the premium automotive industry

This paper summarises the findings of investigations to date in understanding what luxury and premiumness mean to the high-end automotive consumer. Existing writings on luxury and premiumness are considered. An exploratory study was carried out in two countries using 309 respondents and 18 prestige cars. A "stream of consciousness" approach was used to capture respondent's views on a selection of vehicles. The codified transcripts were used to identify key differences between the top and bottom rated vehicles, in terms of the nature and quantity of emotional responses elicited. This paper describes some of these key product differences that were self-reported to impact upon a luxury response. Finally, suggestions are made as to the next steps required for this research. © 2007 ACM.

Characterisation of friction and lubrication regimes in premium tubular connections

A friction test rig has been developed to carry out repeated sliding friction tests for premium tubular connections. The test rig enables accurate measurement of friction in various contact regimes which are relevant to the threaded connections between tubular components. Higher load tests can simulate the contact in metal-to-metal seals under very high contact pressures by using perpendicular pin-on-pin tests. The contact in the thread loading flank under intermediate pressures can be simulated by using larger radius coupon-on-coupon tests. The measured coefficient of friction is well correlated with a lubrication parameter combining lubricant film thickness and initial surface roughness. © 2012 Elsevier Ltd. All rights reserved.

Gaussian process volatility model

The prediction of time-changing variances is an important task in the modeling of financial data. Standard econometric models are often limited as they assume rigid functional relationships for the evolution of the variance. Moreover, functional parameters are usually learned by maximum likelihood, which can lead to over-fitting. To address these problems we introduce GP-Vol, a novel non-parametric model for time-changing variances based on Gaussian Processes. This new model can capture highly flexible functional relationships for the variances. Furthermore, we introduce a new online algorithm for fast inference in GP-Vol. This method is much faster than current offline inference procedures and it avoids overfitting problems by following a fully Bayesian approach. Experiments with financial data show that GP-Vol performs significantly better than current standard alternatives.

Uniform stability of a particle approximation of the optimal filter derivative

Sequential Monte Carlo methods, also known as particle methods, are a widely used set of computational tools for inference in non-linear non-Gaussian state-space models. In many applications it may be necessary to compute the sensitivity, or derivative, of the optimal filter with respect to the static parameters of the state-space model; for instance, in order to obtain maximum likelihood model parameters of interest, or to compute the optimal controller in an optimal control problem. In Poyiadjis et al. [2011] an original particle algorithm to compute the filter derivative was proposed and it was shown using numerical examples that the particle estimate was numerically stable in the sense that it did not deteriorate over time. In this paper we substantiate this claim with a detailed theoretical study. Lp bounds and a central limit theorem for this particle approximation of the filter derivative are presented. It is further shown that under mixing conditions these Lp bounds and the asymptotic variance characterized by the central limit theorem are uniformly bounded with respect to the time index. We demon- strate the performance predicted by theory with several numerical examples. We also use the particle approximation of the filter derivative to perform online maximum likelihood parameter estimation for a stochastic volatility model.

Copula Processes

We define a copula process which describes the dependencies between arbitrarily many random variables independently of their marginal distributions. As an example, we develop a stochastic volatility model, Gaussian Copula Process Volatility (GCPV), to predict the latent standard deviations of a sequence of random variables. To make predictions we use Bayesian inference, with the Laplace approximation, and with Markov chain Monte Carlo as an alternative. We find both methods comparable. We also find our model can outperform GARCH on simulated and financial data. And unlike GARCH, GCPV can easily handle missing data, incorporate covariates other than time, and model a rich class of covariance structures.

Viewing the future

As many industrial organizations have learned to apply roadmapping successfully, they have also learned that it is "roadmapping" rather than "the roadmap" that generates value. This two-part special report has focused primarily on product and technology roadmapping in industry. The first part (RTM, March-April 2003, pp. 26-59) examined the workings of the process at Lucent Technologies, Rockwell Automation, the pharmaceutical/biotechnology industry, and United Kingdom-based Domino Printing Sciences. This second part examines roadmapping in the UK, Motorola, General Motors, the services sector, and in cases that demand major investment decisions under conditions of volatility.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Autoignition of monodisperse biodiesel and diesel sprays in turbulent flows

A new experimental configuration has been developed to examine the effects of flow on the autoignition of dilute diesel and biodiesel sprays, where the spray is injected in the form of monodisperse individual droplets at right angles to a hot air turbulent flow. The ignition location has been measured by monitoring the OH * chemiluminescence. A qualitative comparison of the flame behaviour between ethanol, acetone, heptane and biodiesel as fuels has also been carried out. With decreasing volatility of the fuel, the flame showed progressively a higher number of individual droplets burning, with the first autoignition spots appearing at random locations but in general earlier than the intense droplet-flame emission. The time-averaged autoignition length increased with increasing air velocity and with increasing intensity of the turbulence, while it decreased with the temperature and the droplet size. The data can be used for validating models for two-phase turbulent combustion. © 2012 Elsevier Inc.

Structure of igniting ethanol and n-heptane spray flames with and without swirl

This paper explores the ignition and subsequent evolution of spray flames in a bluff-body configuration with and without swirl. Ethanol and n-heptane are used to compare the effects of volatility. Ignition is performed by a laser spark. High speed imaging of OH *-chemiluminescence and OH-PLIF collected at 5kHz are used to investigate the behaviour of the flames during the first stages of ignition and the stable flame structure following ignition. Swirl induces a wider and shorter flame, precession, and multiple reaction zones, while the non-swirling flames have a simpler structure. The reaction fronts seem thinner with ethanol than with heptane. The dataset can be used for model validation. © 2012 Elsevier Inc.

Simulating early adoption of alternative fuel vehicles for sustainability

We quantify the conditions that might trigger wide spread adoption of alternative fuel vehicles (AFVs) to support energy policy. Empirical review shows that early adopters are heterogeneous motivated by financial benefits, environmental appeal, new technology, and vehicle reliability. A probabilistic Monte Carlo simulation model is used to assess consumer heterogeneity for early and mass market adopters. For early adopters full battery electric vehicles (BEVs) are competitive but unable to surpass diesels or hybrids due to purchase price premium and lack of charging availability. For mass adoption, simulations indicate that if the purchase price premium of a BEV closes to within 20% of an in-class internal combustion engine (ICE) vehicle, combined with a 60% increase in refuelling availability relative to the incumbent system, BEVs become competitive. But this depends on a mass market that values the fuel economy and CO2 reduction benefits associated with BEVs. We also find that the largest influence on early adoption is financial benefit rather than pro-environmental behaviour suggesting that AFVs should be marketed by appealing to economic benefits combined with pro-environmental behaviour to motivate adoption. Monte Carlo simulations combined with scenarios can give insight into diffusion dynamics for other energy demand-side technologies. © 2012 Elsevier Inc.

Investigation into partially premixed combustion in a light-duty multi-cylinder diesel engine fuelled gasoline and diesel with a mixture of

Partially premixed compression ignition (PPCI) engines operating with a low temperature highly homogeneous charge have been demonstrated previously using conventional diesel fuel. The short ignition delay of conventional diesel fuel requires high fuel injection pressures to achieve adequate premixing along with high levels of EGR (exhaust gas recirculation) to achieve low NOx emissions. Low load operating regions are typified by substantial emissions of CO and HC and there exists an upper operating load limitation due to very high rates of in-cylinder gas pressure rise. In this study mixtures of gasoline and diesel fuel were investigated using a multi-cylinder light duty diesel engine. It was found that an increased proportion of gasoline fuel reduced smoke emissions at higher operating loads through an increase in charge premixing resulting from an increase in ignition delay and higher fuel volatility. The results of this investigation confirm that a combination of fuel properties, exhibiting higher volatility and increased ignition delay, would enable a widening of the low emission operating regime, but that consideration must be given to combustion stability at low operating loads. Copyright © 2007 SAE International.