Multicriterion optimal electric drive vehicle selection based on lifecycle emission and lifecycle cost

This research paper examines the optimal choice among conventional gasoline vehicles, hybrid electric vehicles (HEVs), plug‐in HEVs (PHEV), and full‐battery EVs taking into account the different characteristics of these vehicles, such as cost, emissions per mile, and vehicle miles to be traveled between refueling and acceleration time. The existing challenges for wide‐spread deployment of EVs are availability of charging infrastructure, higher cost, long time for charging, and lower travel millage compared with conventional vehicles. Statistical data are considered for determining the spatially varying average daily vehicle miles traveled (VMT) across the United States, which, together with charging behavior, can influence the optimal choice among EV with different travel ranges. Two alternative cases for charging are examined: (1) home‐only charging and (2) home plus work charging. The motivation of this work is to select the optimal EV among their types when lifecycle cost and lifecycle emission are considered. The optimization model seeks to minimize total lifecycle cost and emissions for each level of VMT per day. It is found that when lifecycle cost is the sole objective, HEV is usually the best choice, especially for higher VMT levels. When lifecycle greenhouse gas emission is the sole objective, PHEV1 (PHEV with 1 charging station) is the optimal solution over a wide range of VMTs. The outcome of this provides a roadmap for the selection of EVs based on their annual VMT to reduce both lifecycle emission and lifecycle cost.     

Direct Numerical Simulation of Curly Fibers in Turbulent Channel Flow

Wall deposition of rigid-link fibrous aerosols in a turbulent channel flow is studied. The instantaneous turbulent velocity vector field is generated by the direct numerical simulation of the Navier-Stokes equation with the aid of a pseudospectral code. It is assumed that the fiber is composed of five rigidly attached ellipsoidal links. The dynamic behavior of these elongated and irregular shaped particles is markedly different from the spherical ones. The hydrodynamic forces and torques acting on the fiber are evaluated and the equations governing the translational and rotational motions of the fiber are analyzed. Euler's four parameters are used, and motions of fibrous particles in the turbulent channel flow field are studied. Ensembles of 8000 fiber trajectories are generated and are used for evaluating various statistics. Root mean-square fiber velocities and fiber concentrations at different distances from the wall are evaluated and discussed. Empirical models for the deposition rate of curly fibers are also developed. The model predictions are compared with the simulation data and good agreement is observed.     

Charged Particle Trajectory Statistics and Deposition in a Turbulent Channel Flow

The statistical properties of charged particles and their wall deposition in a turbulent channel flow in the presence of an electrostatic field is studied in this paper. For a dilute concentration, the influence of small particles on the fluid motion is neglected. The instantaneous velocity field is generated by a direct numerical simulation of the Navier-Stokes equation via a pseudospectral method. The case in which each particle carries a single unit of charge and the case in which the particles have a saturation charge distribution are analyzed. Ensembles of 8192 particle trajectories are used for evaluating various statistics. Effects of size and electric field intensity on particle trajectory statistics and wall deposition rate are studied. RMS particle velocities and particle concentrations at different distances from the wall are evaluated and discussed. The results for deposition rates are compared with those obtained from empirical equations.     

A continuum theory for two phase media

Summary A continuum theory of a two phase solid-fluid media is formulated. The basic balance laws for the solid phase as well as for the fluid phase are presented. Based on thermodynamical consideration a set of constitutive equations are derived and the basic equations of motions of the distributed solid and fluid continua are obtained and discussed. It is shown that the theory contains as its special cases, Mohr-Coulomb criterion of limiting equilibrium of granular materials, Saffman theory of dusty gas, as well as Darcy's law of flow through porous media. It is then concluded that the present theory covers the full spectrum of two phase solid-fluid media from low porosity granular media with Darcy's law of fluid motion to low and high concentration two phase flows such as dusty gas and blood flow.     

Design of 2D FIR and IIR Digital Filters with Canonical Signed Digit Coefficients Using Singular Value Decomposition and Genetic Algorithms

In this paper a design approach for two-dimensional finite impulse and infinite impulse response digital filters with canonic signed digit (CSD) coefficients based on singular value decomposition and genetic algorithms (GAs) is presented. The proposed technique uses a new chromosome coding scheme to alleviate the problem encountered by many GAs during crossover and mutation which destroys the CSD property. Examples are given to demonstrate the usefulness of the proposed technique. A comparative study carried out with some of the existing techniques indicates the high throughput property of the proposed technique.     

Exploiting symmetries for scaling loopy belief propagation and relational training

Judging by the increasing impact of machine learning on large-scale data analysis in the last decade, one can anticipate a substantial growth in diversity of the machine learning applications for “big data” over the next decade. This exciting new opportunity, however, also raises many challenges. One of them is scaling inference within and training of graphical models. Typical ways to address this scaling issue are inference by approximate message passing, stochastic gradients, and MapReduce, among others. Often, we encounter inference and training problems with symmetries and redundancies in the graph structure. A prominent example are relational models that capture complexity. Exploiting these symmetries, however, has not been considered for scaling yet. In this paper, we show that inference and training can indeed benefit from exploiting symmetries. Specifically, we show that (loopy) belief propagation (BP) can be lifted. That is, a model is compressed by grouping nodes together that send and receive identical messages so that a modified BP running on the lifted graph yields the same marginals as BP on the original one, but often in a fraction of time. By establishing a link between lifting and radix sort, we show that lifting is MapReduce-able. Still, in many if not most situations training relational models will not benefit from this (scalable) lifting: symmetries within models easily break since variables become correlated by virtue of depending asymmetrically on evidence. An appealing idea for such situations is to train and recombine local models. This breaks long-range dependencies and allows to exploit lifting within and across the local training tasks. Moreover, it naturally paves the way for the first scalable lifted training approaches based on stochastic gradients, both in an online and a MapReduced fashion. On several datasets, the online training, for instance, converges to the same quality solution over an order of magnitude faster, simply because it starts optimizing long before having seen the entire mega-example even once.     

Robustification of input redundant feedback systems using robust actuator weighting in the control allocation problem

In this article, a new methodology for robust actuator weighting in the control allocation (CA) problem of input redundant feedback systems is addressed. The methodology is based on the control structural properties of the plant which were previously used for control configuration selection. Robust performance (RP) measures including H _∞ norm and structured singular value of the closed-loop system are used in this article. The capability of the approach is proven with application to lateral dynamics control of the vehicle over-actuated with front and rear steering systems. Employing the RP measures, it is concluded that the vehicle feedback control with front steering angles gives superior RP properties in comparison with the feedback loop of the rear steering angles. Based on these results, the penalty weightings in the cost function of the CA unit are determined. Simulation results based on nonlinear seven degrees of freedom vehicle handling model show that the selection of penalty weightings in the CA unit based on the RP properties of the control inputs (front and rear steering angles) improves the RP of the closed-loop.     

Task-space control of robots using an adaptive Taylor series uncertainty estimator

An uncertainty estimation and compensation can improve the performance of control systems due to structured and unstructured uncertainty. This paper presents a robust task-space control approach using an adaptive Taylor series uncertainty estimator for electrically driven robot manipulators. It is worth noting that not only the lumped uncertainty is estimated and employed in the indirect form of robust controller, but also the upper bound of approximation error is estimated to form a robustifying term and the asymptotic convergence of tracking error and its time derivative are proven based on stability analysis. Finally, the effectiveness of the proposed controller is shown through simulation and comparison with two valuable control schemes applied on the Selective Compliance Assembly Robot Arm (SCARA) robot manipulator.     

A high-performance switch fabric for integrated circuit and packet switching

This paper describes an architecture for a high-performance switching fabric that can accommodate circuit-switched and packet-switched traffic in a unified manner. The switch fabric is self-routeing and uses fixed-length minipackets within the switching fabric for all types of connections. Its kernel architecture is based on a routeing topology with individual connection paths from all inputs to all outputs and with FIFO queuing at each output. Owing to the disjoint connection paths, there is no internal blocking, and because of output queueing, output port blocking is prevented to a great extent. The uniformity in architecture allows construction of any size fabric from a single basic module which could be realized on a single chip. Larger-size configurations can be realized either as single-stage or multistage configuration. The second part of this paper discusses performance aspects and gives results and dimensioning guidelines for both circuit-switched and packet-switched traffic.