A regenerative multiple zone model for HCCI combustion

A new conserved scalar approach, the so-called regenerative multiple zone (RMZ) model, is introduced to simulate combustion in homogeneous charge compression ignition (HCCI) engines with significant products of combustion. In this approach, two conserved scalars are introduced, the mixture fraction Z and the initial exhaust gas fraction J, to determine uniquely the state of the reactive system as a function of the two conserved scalars and time. For the numerical solution of the HCCI combustion, the conserved scalar plane is divided into different zones, which represent homogeneous reactors with constant initial exhaust gas level. Particularly, the zones are created based on the distribution of the initial exhaust gases and are mixed and regenerated at every time step during combustion in order to account for the history effects which are due to the finite rate chemistry. A proper methodology to create and initialize the new zones during the combustion, the so-called zone creation strategy (ZCS), is also proposed. For validation, the RMZ model is implemented in the 2DRD code, which is a computational fluid dynamics code that solves the governing equations for a two-dimensional reaction-diffusion problem. Initially, the consistency of the new model is validated in a one-dimensional reaction-diffusion (RD) case. Subsequently, the necessity for a proper zone creation strategy is demonstrated by a two-dimensional RD case. Next, a parametric study is performed to investigate the sensitivity of the new model on the maximum number of zones that is used. Finally, the limitations as well as the advantages of the RMZ model are discussed.     

Machine Learning Utilization for Bed Load Transport in Gravel-Bed Rivers

Three data-driven techniques, namely artificial neural networks, adaptive-network-based fuzzy inference system, and symbolic regression based on genetic programming, are employed for the prediction of bed load transport rates in gravel-bed steep mountainous streams and rivers in Idaho (U.S.A.), and the potential of several input variables is investigated. The input combinations that were tested are based, mainly, on unit stream power, stream power, and shear stress, and exhibited similarly good performance, with respect to the machine learning technique used, accentuating the importance of the regression model. The derived models are robust, generalize very well in unseen data, and generated results superior to those of some of the widely used bed load formulae, without the need to set a threshold for the initiation of motion, and consequently avoid predicting erroneous zero transport rates.     

An extended nonparametric homogeneously weighted moving average sign control chart

Parametric (or traditional) control charts are based on the assumption that the quality characteristic of interest follows a specific distribution. However, in many applications, there is a lack of knowledge about the underlying distribution. To this end, nonparametric (or distribution-free) control charts have been developed in recent years. In this article, a nonparametric double homogeneously weighted moving average (DHWMA) control chart based on the sign statistic is proposed for monitoring the location parameter of an unknown and continuous distribution. The performance of the proposed chart is measured through the run-length distribution and its associated characteristics by performing Monte Carlo simulations. The DHWMA sign chart is compared with other nonparametric sign charts, such as the homogeneously weighted moving average, generally weighted moving average (GWMA), double GWMA, and triple exponentially weighted moving average sign charts, as well as the traditional DHWMA chart. The results indicate that the proposed chart performs just as well as and in some cases better than its competitors, especially for small shifts. Finally, two examples are provided to show the application and implementation of the proposed chart.     

A performance evaluation framework for association mining in spatial data

The evaluation of the process of mining associations is an important and challenging problem in database systems and especially those that store critical data and are used for making critical decisions. Within the context of spatial databases we present an evaluation framework in which we use probability distributions to model spatial regions, and Bayesian networks to model the joint probability distribution and the structural relationships among spatial and non-spatial predicates. We demonstrate the applicability of the proposed framework by evaluating representatives from two well-known approaches that are used for learning associations, i.e., dependency analysis (using statistical tests of independence) and Bayesian methods. By controlling the parameters of the framework we provide extensive comparative results of the performance of the two approaches. We obtain measures of recovery of known associations as a function of the number of samples used, the strength, number and type of associations in the model, the number of spatial predicates associated with a particular non-spatial predicate, the prior probabilities of spatial predicates, the conditional probabilities of the non-spatial predicates, the image registration error, and the parameters that control the sensitivity of the methods. In addition to performance we investigate the processing efficiency of the two approaches.     

Matrix hybridization in the interlayer for carbon fiber reinforced composites

Polymeric composites have gone through a level of maturity beyond the laboratory stage with the development of the Boeing 787, the structure of which contains more than 50% composites. Nonetheless, a basic understanding of the material used in its primary structure has not been extensively investigated. For instance, micromechanical models are inadequate as they always assume an evenly distributed homogeneous matrix, without following classic lamination theory, which assumes constant stress through the laminate thickness. Our program now in its third year at the Polymeric Composites Laboratory in Seattle, supported by industry as well as government agencies, and in collaboration with several universities on a global scale, is developing such concepts for understanding and improving matrices in layered configurations. This effort focuses on the development of interlayer systems used as enablers to improve certain properties of the composite, such as fracture‐toughness and crack‐propagation inhibition. POLYM. COMPOS., 31:1965–1976, 2010. © 2010 Society of Plastics Engineers.     

Development of an FPGA-based system for real-time simulation of photovoltaic modules

Photovoltaic (PV) simulators are indispensable for the operational evaluation of PV energy production system components (e.g. battery chargers, DC/AC inverters, etc.), in order to avoid the time-consuming and expensive field-testing process. In this paper, the development of a novel real-time PV simulator based on Field Programmable Gate Arrays (FPGAs), is presented. The proposed system consists of a Buck-type DC/DC power converter, which is controlled by an FPGA-based unit using the Pulse Width Modulation (PWM) principle. The system operator is able to define both the PV module type to be simulated and the environmental conditions under which the selected PV module operates. The proposed design method enhances the rapid system prototyping capability and enables the reduction of the power converter size and cost due to the high clock speed feature of the FPGA-based control unit. The experimental results indicate that, using the proposed method, the PV module current-voltage characteristics examined are reproduced with an average accuracy of 1.03%.     

Carbohydrate moieties on the in vitro immunoreactivity of soy β-conglycinin

β-Conglycinin is a functional glycoprotein and one of the most important soybean allergens. The aim of the present research was to investigate the role of the N-glycans moieties of β-conglycinin on its in vitro immunoreactivity. The soy allergen was obtained by isoelectric precipitation from commercial soy protein isolate and was enzymatically deglycosylated by PNGase F (Peptide N-Glycosidase F EC 3.5.1.52). In order to optimize deglycosylation conditions different reaction times and allergen concentrations were tested. The extent of deglycosylation was estimated by SDS–PAGE, CZE, RP-HPLC, and MALDI-TOF MS analyses, which provided information related to changes in protein structure. The antigenicity of both native β-conglycinin and its deglycosylated form was evaluated by western-blotting and indirect ELISA employing polyclonal rabbit anti-soybean sera and horseradish peroxidase-labeled goat anti-rabbit IgG while the in vitro allergenicity was assessed by means of indirect competitive inhibition ELISA employing human sera (IgE) of soy allergics. β-Conglycinin was effectively deglycosylated by PNGase F. Data on immunological tests suggested that glycosyl moieties forming this glycoprotein might be involved in its immunoreactivity.