Study of steady state and transient EGR behaviour of a medium duty diesel engine

It is well known that accurate EGR control is paramount to controlling engine out emissions during steady state and transient operation of a diesel engine. The direct measurement of EGR is however non-trivial and especially difficult in engines with no external EGR control where the intake manifold CO2 levels can be measured more readily. This work studies the EGR behaviour in a medium duty diesel engine with a passive EGR rebreathing strategy for steady state and transient operation. High speed (response time ∼1ms) in-cylinder sampling using modified GDI valves is coupled with high frequency response analysers to measure the cyclic in-cylinder CO2, from which the EGR rate is deduced. It was found that controlling the EGR using the passive rebreathing strategy during certain combined speed and load transients is challenging, causing high smoke and NO emissions. The in-cylinder sampling method coupled with fast CO2 measurement (time constant ∼8ms) in the exhaust port gave insights about the EGR rate during these transients. The complex interaction of the manifold pressures, turbo-charger operation and trapped charge composition from the previous cycle simply can cause high dilution and therefore high smoke levels. The steady state variation of NO emissions with respect to EGR is also studied using a fast NO analyzer (time constant ∼2ms) in the exhaust port. Cyclic variation was found to be up to ±5% at some load conditions. © 2008 SAE International.

Motor learning of novel dynamics is not represented in a single global coordinate system: evaluation of mixed coordinate representations and local learning.

Successful motor performance requires the ability to adapt motor commands to task dynamics. A central question in movement neuroscience is how these dynamics are represented. Although it is widely assumed that dynamics (e.g., force fields) are represented in intrinsic, joint-based coordinates (Shadmehr R, Mussa-Ivaldi FA. J Neurosci 14: 3208-3224, 1994), recent evidence has questioned this proposal. Here we reexamine the representation of dynamics in two experiments. By testing generalization following changes in shoulder, elbow, or wrist configurations, the first experiment tested for extrinsic, intrinsic, or object-centered representations. No single coordinate frame accounted for the pattern of generalization. Rather, generalization patterns were better accounted for by a mixture of representations or by models that assumed local learning and graded, decaying generalization. A second experiment, in which we replicated the design of an influential study that had suggested encoding in intrinsic coordinates (Shadmehr and Mussa-Ivaldi 1994), yielded similar results. That is, we could not find evidence that dynamics are represented in a single coordinate system. Taken together, our experiments suggest that internal models do not employ a single coordinate system when generalizing and may well be represented as a mixture of coordinate systems, as a single system with local learning, or both.