Analytical evaluation of DC capacitor RMS current and voltage ripple in neutral-point clamped inverters

The sizing of the DC-link capacitor in a three-level inverter is based on the RMS current flowing through it. This paper analyses the DC-link capacitor RMS current in a neutral-point clamped (NPC) inverter and expresses the same as a function of modulation index, line-side current amplitude and power factor. Analytical closed-form expressions are derived for the capacitor RMS current for single-phase half-bridge, single-phase full-bridge and three-phase three-leg topologies of a three-level inverter. The worst-case capacitor current stress is determined for each topology based on the analytical expressions. Further, analytical expressions are derived for the RMS values of low-frequency and high-frequency capacitor currents. These expressions are then used to estimate voltage ripple across the DC capacitor for sinusoidally modulated three-phase NPC inverter. The analytical expressions for the RMS current and voltage ripple are validated experimentally over a wide range of operating points.     

Finite element modeling of environmental effects on rigid pavement deformation

In this study, finite element (FE)-based primary pavement response models are employed for investigating the early-age deformation characteristics of jointed plain concrete pavements (JPCP) under environmental effects. The FE-based ISLAB (two-and-one-half-dimensional) and EverFE (three-dimensional) software were used to conduct the response analysis. Sensitivity analyses of input parameters used in ISLAB and EverFE were conducted based on field and laboratory test data collected from instrumented pavements on highway US-34 near Burlington, Iowa. Based on the combination of input parameters and equivalent temperatures established from preliminary studies, FE analyses were performed and compared with the field measurements. Comparisons between field measured and computed deformations showed that both FE programs could produce reasonably accurate estimates of actual slab deformations due to environmental effects using the equivalent temperature difference concept.     

Temperature diagnostics of ECR plasma by measurement of electron bremsstrahlung

The x-ray bremsstrahlung spectrum emitted by the electron population in a 14.5 GHz ECR plasma source has been measured using a NaI(Tl) detector, and hence the electron temperature of the higher energy electron population in the plasma has been determined. The x-ray spectra for Ne and Ar gases have been systematically studied as a function of inlet gas pressure from 7 × 10(-7) mbar to 7 × 10(-5) mbar and for input microwave power ～1 W to ～300 W. At the highest input power and optimum pressure conditions, the end point bremsstrahlung energies are seen to reach ～700 keV. The estimated electron temperatures (T(e)) were found to be in the range 20 keV-80 keV. The T(e) is found to be peaking at a pressure of 1 × 10(-5) mbar for both gases. The T(e) is seen to increase with increasing input power in the intermediate power region, i.e., between 100 and 200 W, but shows different behaviour for different gases in the low and high power regions. Both gases show very weak dependence of electron temperature on inlet gas pressure, but the trends in each gas are different.     

Support Vector Machines Approach to HMA Stiffness Prediction

The application of artificial intelligence (AI) techniques to engineering has increased tremendously over the last decade. Support vector machine (SVM) is one efficient AI technique based on statistical learning theory. This paper explores the SVM approach to model the mechanical behavior of hot-mix asphalt (HMA) owing to high degree of complexity and uncertainty inherent in HMA modeling. The dynamic modulus (|E?|), among HMA mechanical property parameters, not only is important for HMA pavement design but also in determining HMA pavement performance associated with pavement response. Previously employed approaches for development of the predictive |E?| models concentrated on multivariate regression analysis of database. In this paper, SVM-based |E?| prediction models were developed using the latest comprehensive |E?| database containing 7,400 data points from 346 HMA mixtures. The developed SVM models were compared with the existing multivariate regression-based |E?| model as well as the artificial neural networks (ANN) based |E?| models developed recently by the writers. The prediction performance of SVM model is better than multivariate regression-based model and comparable to the ANN. Fewer constraints in SVM compared to ANN can make it a promising alternative considering the availability of limited and nonrepresentative data frequently encountered in construction materials characterization.     

Ba3(P1 − x Mn x O4)2 : Blue/green inorganic materials based on tetrahedral Mn(V)

We describe a blue/green inorganic material, Ba₃(P_1???x Mn _x O₄)₂ (I) based on tetrahedral MnO $_{4}^{3-}$ :3d ²chromophore. The solid solutions (I) which are sky-blue and turquoise-blue for x ≤ 0·25 and dark green for x ≥ 0·50, are readily synthesized in air from commonly available starting materials, stabilizing the MnO $_{4}^{3-}$ chromophore in an isostructural phosphate host. We suggest that the covalency/ionicity of P–O/Mn–O bonds in the solid solutions tunes the crystal field strength around Mn(V) such that a blue colour results for materials with small values of x. The material could serve as a nontoxic blue/green inorganic pigment.     

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.     

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

This paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely shuffled complex evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. SCE, a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a neural networks surrogate finite-element based forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. The proposed rigid pavement backcalculation model is evaluated using field non-destructive test data acquired from a full-scale airport pavement test facility.     

Optimal Management of Water for Sustainable Fisheries and Aquaculture

This paper discusses optimal management for water resources in the context of the over‐exploitation of fish and the pollution of freshwater fisheries, along with aquaculture, in inland and coastal areas. An optimal control model maximizing social surplus focusing on the dynamic relationship of fish stock externality, price, outputs, costs and polluting wastes is developed. It provides the necessary conditions for optimal management for both fishery and aquaculture, which produce the same product for the market, and also develops time‐paths and phase diagrams for policy application.     

Influence of different types of water on strength,porosity and hydric parameters of metakaolin admixtured cement

The present research deals with strength, porosity and hydric behavior of metakaolin cement admixtured with different types of water. The hydration of ordinary Portland cement in the presence of 0%, 10%, 20% and 30% metakaolin treated with distilled, ground and sea water with the water to cement ratio of 0.4 was studied. The experimental results on setting time, strength, porosity and hydric parameters are reported. The results show that, metakaolin percentage increases in strength with a decrease in porosity. The observed results are discussed with SEM micrographs. Further, sea water accelerates the cement hydration at the early stages but retards it in the latter stages of hydration.     

An efficient technique to solve combined economic and emission dispatch problem using modified Ant colony optimization

Economic load dispatch is one of the vital purposes in electrical power system operation, management and planning. Economic dispatch problem is one of the most important problems in electric power system operation. In large scale system, the problem is more complex and difficult to find out optimal solution because it is nonlinear function and it contains number of local optimal. Combined economic emission dispatch (CEED) problem is to schedule the committed generating units outputs to meet the required load demand at minimum operating cost with minimum emission simultaneously. The main aim of economic load dispatch is to reduce the total production cost of the generating system and at the same time the necessary equality and inequality constraints should also be fulfilled. This leads to the development of CEED techniques. There are various techniques proposed by several researchers to solve CEED problem based on optimization techniques. But still some problems such as slower convergence and higher computational complexity exist in using the optimization techniques such as GA for solving CEED problem. This paper proposes an efficient and reliable technique for combined fuel cost economic optimization and emission dispatch using the Modified Ant Colony Optimization algorithm (MACO) to produce better optimal solution. The simulation results reveal the significant performance of the proposed MACO approach.