Dipolar emission in trench metal-insulator-metal waveguides for short-scale plasmonic communications: numerical optimization

Here we consider the numerical optimization of active surface plasmon polariton (SPP) trench waveguides suited for integration with luminescent polymers for use as highly localized SPP source devices in short-scale communication integrated circuits. The numerical analysis of the SPP modes within trench waveguide systems provides detailed information on the mode field components, effective indices, propagation lengths and mode areas. Such trench waveguide systems offer extremely high confinement with propagation on length scales appropriate to local interconnects, along with high efficiency coupling of dipolar emitters to waveguided plasmonic modes which can be close to 80%. The large Purcell factor exhibited in these structures will further lead to faster modulation capabilities along with an increased quantum yield beneficial for the proposed plasmon-emitting diode, a plasmonic analog of the light-emitting diode. The confinement of studied guided modes is on the order of 50 nm and the delay over the shorter 5 μm length scales will be on the order of 0.1 ps for the slowest propagating modes of the system, and significantly less for the faster modes.

Day-Ahead Optimal Charging/Discharging Scheduling for Electric Vehicles in Micro-Grids

A micro-grid is an autonomous system which can be operated and connected to an external system or isolated with the help of energy storage systems (ESSs). While the daily output of distributed generators (DGs) strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the imbalance between the daily load and generation curves. In this paper, a statistical model is presented to describe daily EV charging/discharging behaviour. An optimisation problem is proposed to obtain economic operation for the micro-grid based on this model. In day-ahead scheduling, with estimated information of power generation and load demand, optimal charging/discharging of EVs during 24 hours is obtained. A series of numerical optimization solutions in different scenarios is achieved by serial quadratic programming. The results show that optimal charging/discharging of EVs, a daily load curve can better track the generation curve and the network loss and required ESS capacity are both decreased. The paper also demonstrates cost benefits for EVs and operators.

Covariance Averaging for Improved Estimation and Portfolio Allocation

We propose a new method for estimating the covariance matrix of a multivariate time series of nancial returns. The method is based on estimating sample covariances from overlapping windows of observations which are then appropriately weighted to obtain the nal covariance estimate. We extend the idea of (model) covariance averaging o ered in the covariance shrinkage approach by means of greater ease of use, exibility and robustness in averaging information over different data segments. The suggested approach does not su er from the curse of dimensionality and can be used without problems of either approximation or any demand for numerical optimization.

Optimal Scheduling Methods to Integrate Plug-In Electric Vehicles with the Power System: A Review

The introduction of the Tesla in 2008 has demonstrated to the public of the potential of electric vehicles in terms of reducing fuel consumption and green-house gas from the transport sector. It has brought electric vehicles back into the spotlight worldwide at a moment when fossil fuel prices were reaching unexpected high due to increased demand and strong economic growth. The energy storage capabilities from of fleets of electric vehicles as well as the potentially random discharging and charging offers challenges to the grid in terms of operation and control. Optimal scheduling strategies are key to integrating large numbers of electric vehicles and the smart grid. In this paper, state-of-the-art optimization methods are reviewed on scheduling strategies for the grid integration with electric vehicles. The paper starts with a concise introduction to analytical charging strategies, followed by a review of a number of classical numerical optimization methods, including linear programming, non-linear programming, dynamic programming as well as some other means such as queuing theory. Meta-heuristic techniques are then discussed to deal with the complex, high-dimensional and multi-objective scheduling problem associated with stochastic charging and discharging of electric vehicles. Finally, future research directions are suggested.

Numerical Method for Cost-Weight Optimisation of Stringer-Skin Panels

The need to integrate cost into the early product definition process as an engineering parameter is addressed. The application studied is a fuselage panel that is typical for commercial transport regional jets. Consequently, a semi-empirical numerical analysis using reference data was coupled to model the structural integrity of thin-walled structures with regard to material failure and buckling: skin, stringer, flexural, and interrivet. The optimization process focuses on direct operating cost (DOC) as a function of acquisition cost and fuel burn. It was found that the ratio of acquisition cost to fuel burn was typically 4:3 and that there was a 10% improvement in the DOC for the minimal DOC condition over the minimal weight condition because of the manufacturing cost saving from having a reduced number of larger-area stringers and a slightly thicker skin than that preferred by the minimal weight condition. Also note that the minimal manufacturing cost condition was slightly better than the minimal weight condition, which highlights the key finding: The traditional minimal weight condition is a dated and suboptimal approach to airframe structural design.

Uncertainty and Sensitivity Analysis in Aircraft Operating Costs in Structural Design Optimization

The paper focuses on the development of an aircraft design optimization methodology that models uncertainty and sensitivity analysis in the tradeoff between manufacturing cost, structural requirements, andaircraft direct operating cost.Specifically,ratherthanonlylooking atmanufacturingcost, direct operatingcost is also consideredintermsof the impact of weight on fuel burn, in addition to the acquisition cost to be borne by the operator. Ultimately, there is a tradeoff between driving design according to minimal weight and driving it according to reduced manufacturing cost. Theanalysis of cost is facilitated withagenetic-causal cost-modeling methodology,andthe structural analysis is driven by numerical expressions of appropriate failure modes that use ESDU International reference data. However, a key contribution of the paper is to investigate the modeling of uncertainty and to perform a sensitivity analysis to investigate the robustness of the optimization methodology. Stochastic distributions are used to characterize manufacturing cost distributions, andMonteCarlo analysis is performed in modeling the impact of uncertainty on the cost modeling. The results are then used in a sensitivity analysis that incorporates the optimization methodology. In addition to investigating manufacturing cost variance, the sensitivity of the optimization to fuel burn cost and structural loading are also investigated. It is found that the consideration of manufacturing cost does make an impact and results in a different optimal design configuration from that delivered by the minimal-weight method. However, it was shown that at lower applied loads there is a threshold fuel burn cost at which the optimization process needs to reduce weight, and this threshold decreases with increasing load. The new optimal solution results in lower direct operating cost with a predicted savings of 640=m2 of fuselage skin over the life, relating to a rough order-of-magnitude direct operating cost savings of $500,000 for the fuselage alone of a small regional jet. Moreover, it was found through the uncertainty analysis that the principle was not sensitive to cost variance, although the margins do change.

Generation and optimization of electron currents along the walls of a conical target for fast ignition

The interaction of an ultraintense laser pulse with a conical target is studied by means of numerical particle-in-cell simulations in the context of fast ignition. The divergence of the fast electron beam generated at the tip of the cone has been shown to be a crucial parameter for the efficient coupling of the ignition laser pulse to the precompressed fusion pellet. In this paper, we demonstrate that a focused hot electron beam is produced at the cone tip, provided that electron currents flowing along the surfaces of the cone sidewalls are efficiently generated. The influence of various interaction parameters over the formation of these wall currents is investigated. It is found that the strength of the electron flows is enhanced for high laser intensities, low density targets, and steep density gradients inside the cone. The hot electron energy distribution obeys a power law for energies of up to a few MeV, with the addition of a high-energy Maxwellian tail.

Two-stage RBF network construction based on particle swarm optimization

The conventional radial basis function (RBF) network optimization methods, such as orthogonal least squares or the two-stage selection, can produce a sparse network with satisfactory generalization capability. However, the RBF width, as a nonlinear parameter in the network, is not easy to determine. In the aforementioned methods, the width is always pre-determined, either by trial-and-error, or generated randomly. Furthermore, all hidden nodes share the same RBF width. This will inevitably reduce the network performance, and more RBF centres may then be needed to meet a desired modelling specification. In this paper we investigate a new two-stage construction algorithm for RBF networks. It utilizes the particle swarm optimization method to search for the optimal RBF centres and their associated widths. Although the new method needs more computation than conventional approaches, it can greatly reduce the model size and improve model generalization performance. The effectiveness of the proposed technique is confirmed by two numerical simulation examples.

Optimization of Composite Structures to Delay Mode Jump Instabilities

Numerous studies have shown that postbuckling stiffened panels may undergo abrupt changes in buckled mode
shape when loaded in uniaxial compression. This phenomenon is often referred to as a mode jump or secondary
instability. The resulting sudden release of stored energy may initiate damage in vulnerable regions within a
structure, for example, at the skin-stiffener interface of a stiffened composite panel. Current design practice is to
remove a mode jump by increasing the skin thickness of the postbuckling region. A layup optimization methodology,
based on a genetic algorithm, is presented, which delays the onset of secondary instabilities in a composite structure
while maintaining a constant weight and subject to a number of design constraints. A finite element model was
developed of a stiffened panel’s skin bay, which exhibited secondary instabilities. An automated numerical routine
extracted information directly from the finite element displacement results to detect the onset of initial buckling and
secondary instabilities. This routine was linked to the genetic algorithm to find a revised layup for the skin bay, within
appropriate design constraints, to delay the onset of secondary instabilities. The layup optimization methodology,
resulted in a panel that had a higher buckling load, prebuckling stiffness, and secondary instability load than the
baseline design.

Lay-up optimization of composite plates to delay mode-jump instabilities

Experimental and numerical studies have shown that the occurrence of abrupt secondary instabilities, or mode-jumps, in a postbuckling stiffened composite panel may initiate structural failure. This study presents an optimisation methodology, using a genetic algorithm and finite element analysis for the lay-up optimisation of postbuckling composite plates to delay the onset of mode-jump instabilities. A simple and novel approach for detecting modejumps is proposed, based on the RMS value of out-of-plane pseudo-velocities at a number of locations distributed over the postbuckling structure

DFIG Machine Design for Maximizing Power Output Based on Surrogate Optimization Algorithm

This paper presents a surrogate-model based optimization of a doubly-fed induction generator (DFIG) machine winding design for maximizing power yield. Based on site-specific wind profile data and the machine’s previous operational performance, the DFIG’s stator and rotor windings are optimized to match the maximum efficiency with operating conditions for rewinding purposes. The particle swarm optimization (PSO)-based surrogate optimization techniques are used in conjunction with the finite element method (FEM) to optimize the machine design utilizing the limited available information for the site-specific wind profile and generator operating conditions. A response surface method in the surrogate model is developed to formulate the design objectives and constraints. Besides, the machine tests and efficiency calculations follow IEEE standard 112-B. Numerical and experimental results validate the effectiveness of the proposed technologies.

A self-learning teaching-learning based optimization for dynamic economic/environmental dispatch considering multiple plug-in electric vehicle loads

Economic and environmental load dispatch aims to determine the amount of electricity generated from power plants to meet load demand while minimizing fossil fuel costs and air pollution emissions subject to operational and licensing requirements. These two scheduling problems are commonly formulated with non-smooth cost functions respectively considering various effects and constraints, such as the valve point effect, power balance and ramp rate limits. The expected increase in plug-in electric vehicles is likely to see a significant impact on the power system due to high charging power consumption and significant uncertainty in charging times. In this paper, multiple electric vehicle charging profiles are comparatively integrated into a 24-hour load demand in an economic and environment dispatch model. Self-learning teaching-learning based optimization (TLBO) is employed to solve the non-convex non-linear dispatch problems. Numerical results on well-known benchmark functions, as well as test systems with different scales of generation units show the significance of the new scheduling method.

A biogeography-based optimization algorithm with mutation strategies for model parameter estimation of solar and fuel cells

Mathematical models are useful tools for simulation, evaluation, optimal operation and control of solar cells and proton exchange membrane fuel cells (PEMFCs). To identify the model parameters of these two type of cells efficiently, a biogeography-based optimization algorithm with mutation strategies (BBO-M) is proposed. The BBO-M uses the structure of biogeography-based optimization algorithm (BBO), and both the mutation motivated from the differential evolution (DE) algorithm and the chaos theory are incorporated into the BBO structure for improving the global searching capability of the algorithm. Numerical experiments have been conducted on ten benchmark functions with 50 dimensions, and the results show that BBO-M can produce solutions of high quality and has fast convergence rate. Then, the proposed BBO-M is applied to the model parameter estimation of the two type of cells. The experimental results clearly demonstrate the power of the proposed BBO-M in estimating model parameters of both solar and fuel cells.