Investigation of transition between spark ignition and controlled auto-ignition combustion in a V6 direct-injection engine with cam profile switching

Controlled auto-ignition (CAI) combustion, also known as Homogeneous Charge Compression Ignition (HCCI) can be achieved by trapping residuals with early exhaust valve closure in a direct fuel injection in-cylinder four-stroke gasoline engines (through the employment of low-lift cam profiles). Due to the operating region being limited to low and mid-load operation for CAI combustion with a low-lift cam profile, it is important to be able to operate SI combustion at high-load with a normal cam profile. A 3.0L prototype engine was modified to achieve CAI combustion, using a Cam Profile Switching mechanism which has the capability to switch between high and low-lift cam-profiles. A strategy was used where a high-profile could be used for SI combustion and a low-lift profile was used for CAI combustion. Initial analysis showed that for transitioning from SI to CAI combustion, misfire occurred on the first CAI transitional cycle. Subsequent experiments showed that the throttle opening position and switching time could be controlled avoiding misfire. Further work investigated transitioning at different loads and from CAI to SI combustion

On controllability of neuronal networks with constraints on the average of control gains

Control gains play an important role in the control of a natural or a technical system since they reflect how much resource is required to optimize a certain control objective. This paper is concerned with the controllability of neuronal networks with constraints on the average value of the control gains injected in driver nodes, which are in accordance with engineering and biological backgrounds. In order to deal with the constraints on control gains, the controllability problem is transformed into a constrained optimization problem (COP). The introduction of the constraints on the control gains unavoidably leads to substantial difficulty in finding feasible as well as refining solutions. As such, a modified dynamic hybrid framework (MDyHF) is developed to solve this COP, based on an adaptive differential evolution and the concept of Pareto dominance. By comparing with statistical methods and several recently reported constrained optimization evolutionary algorithms (COEAs), we show that our proposed MDyHF is competitive and promising in studying the controllability of neuronal networks. Based on the MDyHF, we proceed to show the controlling regions under different levels of constraints. It is revealed that we should allocate the control gains economically when strong constraints are considered. In addition, it is found that as the constraints become more restrictive, the driver nodes are more likely to be selected from the nodes with a large degree. The results and methods presented in this paper will provide useful insights into developing new techniques to control a realistic complex network efficiently

BMB activity defines refractory and competent hair follicle populations during the propagation of regenerative waves

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Guiding chemical pulses through geometry: Y-junctions

We study computationally and experimentally the propagation of chemical pulses in complex geometries.The reaction of interest, CO oxidation, takes place on single crystal Pt(110) surfaces that are microlithographically patterned; they are also addressable through a focused laser beam, manipulated through galvanometer mirrors, capable of locally altering the crystal temperature and thus affecting pulse propagation. We focus on sudden changes in the domain shape (corners in a Y-junction geometry) that can affect the pulse dynamics; we also show how brief, localized temperature perturbations can be used to control reactive pulse propagation.The computational results are corroborated through experimental studies in which the pulses are visualized using Reflection Anisotropy Microscopy.Comment: submitted to Phys. Rev.

Dispersion Relations for Thermally Excited Waves in Plasma Crystals

Thermally excited waves in a Plasma crystal were numerically simulated using a Box_Tree code. The code is a Barnes_Hut tree code proven effective in modeling systems composed of large numbers of particles. Interaction between individual particles was assumed to conform to a Yukawa potential. Particle charge, mass, density, Debye length and output data intervals are all adjustable parameters in the code. Employing a Fourier transform on the output data, dispersion relations for both longitudinal and transverse wave modes were determined. These were compared with the dispersion relations obtained from experiment as well as a theory based on a harmonic approximation to the potential. They were found to agree over a range of 0.9<k<5, where k is the shielding parameter, defined by the ratio between interparticle distance a and dust Debye length lD. This is an improvement over experimental data as current experiments can only verify the theory up to k = 1.5.Comment: 8 pages, Presented at COSPAR '0