Machine learning for predictive condensed-phase simulation

We show how machine learning techniques based on Bayesian inference can be used to reach new levels of realism in the computer simulation of molecular materials, focusing here on water. We train our machine-learning algorithm using accurate, correlated quantum chemistry, and predict energies and forces in molecular aggregates ranging from clusters to solid and liquid phases. The widely used electronic-structure methods based on density-functional theory (DFT) give poor accuracy for molecular materials like water, and we show how our techniques can be used to generate systematically improvable corrections to DFT. The resulting corrected DFT scheme gives remarkably accurate predictions for the relative energies of small water clusters and of different ice structures, and greatly improves the description of the structure and dynamics of liquid water.

Autoignition of alcohols and ethers in a rapid compression machine

The autoignition characteristics of methanol, ethanol and MTBE (methyl tert-butyl ether) have been investigated in a rapid compression machine at pressures in the range 20-40 atm and temperatures within 750-1000 K. All three oxygenated fuels tested show higher autoignition temperatures than paraffins, a trend consistent with the high octane number of these fuels. The autoignition delay time for methanol was slightly lower than predicted values using reported reaction mechanisms. However, the experimental and measured values for the activation energy are in very good agreement around 44 kcal/mol. The measured activation energy for ethanol autoignition is in good agreement with previous shock tube results (31 kcal/mol), although ignition times predicted by the shock tube correlation are a factor of three lower than the measured values. The measured activation energy for MTBE, 41.4 kcal/mol, was significantly higher than the value previously observed in shock tubes (28.1 kcal/mol). The onset of preignition, characterized by a slow energy release prior to early ignition was observed in some instances. Possible reasons for these ocurrences are discussed. © Copyright 1993 Society of Automotive engineers, Inc.

Dynamic modelling of the brushless doubly fed machine

A coupled-circuit model for the brushless doubly fed machine (BDFM) has been developed. The transformation of the model into the d-q axis form, ultimately in a synchronous reference frame in which machine currents and voltages have constant values in the steady state, has been carried out. A model-reduction technique is presented, which gives a concise representation of the 'nested-loop' rotor design using a single d-q pair. These models have been experimentally verified and give a convenient and accurate way of calculating the dynamic behaviour of a BDFM. The ability to represent the BDFM with a single d-q pair considerably simplifies the design of suitable controllers. © The Institution of Engineering and Technology 2013.

Developing ISO 14649-based conversational programming system for multi-channel complex machine tools

A multi-channel complex machine tool (MCCM) is a versatile machining system equipped with more than two spindles and turrets for both turning and milling operations. Despite the potential of such a tool, the value of the hardware is largely dependent on how the machine tools are effectively programmed for machining. In this paper we consider a shop-floor programming system based on ISO 14649 (called e-CAM), the international standard for the interface between computer-aided manufacture (CAM) and computer numerical control (CNC). To be deployed in practical industrial usage a great deal of research has to be carried out. In this paper we present: 1) Design consideration for an e-CAM system, 2) The architecture design of e-CAM, 3) Major algorithms to fulfill the modules defined in the architecture, and 4) Implementation details.