ALBERT

Description: Locating sources of diffusion and spreading from minimum data is a significant problem in network science with great applied values to the society. However, a general theoretical framework dealing with optimal source localization is lacking. Combining the controllability theory for complex networks and compressive sensing, we develop a framework with high efficiency and robustness for optimal source localization in arbitrary weighted networks with arbitrary distribution of sources. We offer a minimum output analysis to quantify the source locatability through a minimal number of messenger nodes that produce sufficient measurement for fully locating the sources. When the minimum messenger nodes are discerned, the problem of optimal source localization becomes one of sparse signal reconstruction, which can be solved using compressive sensing. Application of our framework to model and empirical networks demonstrates that sources in homogeneous and denser networks are more readily to be located. A surprising finding is that, for a connected undirected network with random link weights and weak noise, a single messenger node is sufficient for locating any number of sources. The framework deepens our understanding of the network source localization problem and offers efficient tools with broad applications.

Description: Lower Cretaceous pedogenic carbonates exposed in SE China have been dated by U–Pb isotope measurements on single zircons taken from intercalated volcanic rocks, and the ages integrated with existing stratigraphy. 13 C values of calcretes range from –7.0 to –3.0 and can be grouped into five episodes of increasing–decreasing values. The carbon isotope proxy derived from these palaeosol carbonates suggests p CO 2 mostly in the range 1000–2000 parts per million by volume (ppmV) at S ( z ) (CO 2 contributed by soil respiration) = 2500 ppmV and 25°C during the Hauterivian–Albian interval ( c . 30 Ma duration). Such atmospheric CO 2 levels are 4–8 times pre-industrial values, almost double those estimated by geochemical modelling and much higher than those established from stomatal indices in fossil plants. Rapid rises in p CO 2 are identified for early Hauterivian, middle Barremian, late Aptian, early Albian and middle Albian time, and rapid falls for intervening periods. These episodic cyclic changes in p CO 2 are not attributed to local tectonism and volcanism but rather to global changes. The relationship between reconstructed p CO 2 and the development of large igneous provinces (LIPs) remains unclear, although large-scale extrusion of basalt may well be responsible for relatively high atmospheric levels of this greenhouse gas. Suggested levels of relatively low p CO 2 correspond in timing to intervals of regional to global enrichment of marine carbon in sediments and negative carbon isotope ( 13 C) excursions characteristic of the oceanic anoxic events OAE1a (Selli Event), Kilian and Paquier events (constituting part of the OAE 1b cluster) and OAE1d. Short-term episodes of high p CO 2 coincide with negligible carbon isotope excursions associated with the Faraoni Event and the Jacob Event. Given that episodes of regional organic carbon burial would draw down CO 2 and negative 13 C excursions indicate the addition of isotopically light carbon to the ocean–atmosphere system, controls on the carbon cycle in controlling p CO 2 during Early Cretaceous time were clearly complex and made more so by atmospheric composition also being affected by changes in silicate weathering intensity.

Description: In this work, two kinds of partial least squares modelling methods are applied to predict a compressor map: one uses a power function polynomial as the basis function (PLSO), and the other uses a trigonometric function polynomial (PLSN). To demonstrate the potential capabilities of PLSO and PLSN for a typical interpolated prediction and an extrapolated prediction, they are compared with two other classical data-driven modelling methods, namely the look-up table and artificial neural network (ANN). PLSO and PLSN are also compared with each other. The results show that PLSO and PLSN have a better prediction performance than the look-up table and the ANN, especially for the extrapolated prediction. The computational time is also decreased sharply. Compared with PLSO, PLSN is characterized by a higher prediction accuracy and shorter computational time than PLSO. It is expected that PLSN could save computational time and also improve the accuracy of a thermodynamic model of a diesel engine.

Description: Aimed at the problem of exhaust gas recirculation (EGR) performance evaluation and optimal EGR rate determination of turbocharged diesel engines, an optimized decision-making method, based on grey theory and entropy weight, was proposed. The internal combustion pressure, fuel consumption rate, NO X , CO and smoke were selected as the decision-making targets and the initial decision-making model was established based on the traditional grey decision-making theory. According to the characteristics and optimization requirements of EGR, the optimal compromise between combustion and emission performance is proposed to transform into decision-making target weighting problem, then an optimized subjective weighting method based on expert scoring and grey relational analysis is proposed. Finally, the entropy weight method was used to solve the objective weight and the optimized multi-objective grey decision-making model was established, which can not only weaken the human error of subjective empowerment, but also fully explore the intrinsic relationship of the evaluation indexes. At last, an optimization simulation platform for EGR performance evaluation based on MATLB/GUIDE was designed and established. The results show that the optimization simulation platform can effectively improve the efficiency of simulation calculation, which is more convenient for practical engineering applications. The optimized method can successfully realize EGR performance evaluation and optimal EGR rate determination under different working conditions. The decision-making result was consistent with the present EGR control strategies, which provide a new research idea for EGR performance optimization.

Description: To achieve a much more extensive intake air flow range of the diesel engine, a variable-geometry compressor (VGC) is introduced into a turbocharged diesel engine. However, due to the variable diffuser vane angle (DVA), the prediction for the performance of the VGC becomes more difficult than for a normal compressor. In the present study, a prediction model comprising an elliptical equation and a PLS (partial least-squares) model was proposed to predict the performance of the VGC. The speed lines of the pressure ratio map and the efficiency map were fitted with the elliptical equation, and the coefficients of the elliptical equation were introduced into the PLS model to build the polynomial relationship between the coefficients and the relative speed, the DVA. Further, the maximal order of the polynomial was investigated in detail to reduce the number of sub-coefficients and achieve acceptable fit accuracy simultaneously. The prediction model was validated with sample data and in order to present the superiority of compressor performance prediction, the prediction results of this model were compared with those of the look-up table and back-propagation neural networks (BPNNs). The validation and comparison results show that the prediction accuracy of the new developed model is acceptable, and this model is much more suitable than the look-up table and the BPNN methods under the same condition in VGC performance prediction. Moreover, the new developed prediction model provides a novel and effective prediction solution for the VGC and can be used to improve the accuracy of the thermodynamic model for turbocharged diesel engines in the future.

Description: This paper describes an investigation of the dynamics and acoustics of cloud cavitation, the structures which are often formed by the periodic breakup and collapse of a sheet or vortex cavity. This form of cavitation frequently causes severe noise and damage, though the precise mechanism responsible for the enhancement of these adverse effects is not fully understood. In this paper, we investigate the large impulsive surface pressures generated by this type of cavitation and correlate these with the images from high-speed motion pictures. This reveals that several types of propagating structures (shock waves) are formed in a collapsing cloud and dictate the dynamics and acoustics of collapse. One type of shock wave structure is associated with the coherent collapse of a well-defined and separate cloud when it is convected into a region of higher pressure. This type of global structure causes the largest impulsive pressures and radiated noise. But two other types of structure, termed 'crescent-shaped regions' and leading-edge structures' occur during the less-coherent collapse of clouds. These local events are smaller and therefore produce less radiated noise but the interior pressure pulse magnitudes are almost as large as those produced by the global events. The ubiquity and severity of these propagating shock wave structures provides a new perspective on the mechanisms reponsible for noise and damage in cavitating flows involving clouds of bubbles. It would appear that shock wave dynamics rather than the collapse dynamics of single bubbles determine the damage and noise in many cavitating flows.