Determination of optimal reversed field with maximal electrocaloric cooling by a direct entropy analysis

Application of a negative field on a positively poled ferroelectric sample can enhance the electrocaloric cooling and appears as a promising method to optimize the electrocaloric cycle. Experimental measurements show that the maximal cooling does not appear at the zero-polarization point, but around the shoulder of the P-E loop. This phenomenon cannot be explained by the theory based on the constant total entropy assumption under adiabatic condition. In fact, adiabatic condition does not imply constant total entropy when irreversibility is involved. A direct entropy analysis approach based on work loss is proposed in this work, which takes the entropy contribution of the irreversible process into account. The optimal reversed field determined by this approach agrees with the experimental observations. This study signifies the importance of considering the irreversible process in the electrocaloric cycles

MINLP Synthesis of Processes for the Production of Biogas from Organic and Animal Waste

This paper describes a superstructure approach for the synthesis of biogas processes simultaneously with the selection of different process background alternatives. The superstructure consists of anaerobic fermentation under thermophilic or mesophilic conditions, including options for a rendering plant, with different organic and animal wastes from either existing or new plants, different water supplies, wastewater treatments and biogas usage options. An aggregated mathematical model with an economic objective function, formulated as a mixed-integer nonlinear programming (MINLP) problem, was developed. An industrial case study was applied to an existing large-scale meat company, in order to describe the mathematical model and illustrate the MINLP synthesis approach. The optimal solution indicates that significant benefit can be obtained if biogas processes are selected simultaneously with the selection of different process background alternatives thus yielding the optimal integration of biogas processes with their background

Determination of the magnetic anisotropy axes of single-molecule magnets

Simple methods are presented allowing the determination of the magnetic anisotropy axes of a crystal of a single-molecule magnet (SMM). These methods are used to determine an upper bound of the easy axis tilts in a standard Mn12-Ac crystal. The values obtained in the present study are significately smaller than those reported in recent high frequency electron paramagnetic resonance (HF-EPR) studies which suggest distributions of hard-axes tilts.Comment: 10 pages, 6 figure

From laterally modulated two-dimensional electron gas towards artificial graphene

Cyclotron resonance has been measured in far-infrared transmission of GaAs/Al$_x$Ga$_{1-x}$As heterostructures with an etched hexagonal lateral superlattice. Non-linear dependence of the resonance position on magnetic field was observed as well as its splitting into several modes. Our explanation, based on a perturbative calculation, describes the observed phenomena as a weak effect of the lateral potential on the two-dimensional electron gas. Using this approach, we found a correlation between parameters of the lateral patterning and the created effective potential and obtain thus insights on how the electronic miniband structure has been tuned. The miniband dispersion was calculated using a simplified model and allowed us to formulate four basic criteria that have to be satisfied to reach graphene-like physics in such systems

Investigating prior parameter distributions in the inverse modelling of water distribution hydraulic models

PublishedJournal Article© 2014 Journal of Mechanical Engineering. All rights reserved. Inverse modelling concentrates on estimating water distribution system (WDS) model parameters that are not directly measurable, e.g. pipe roughness coefficients, which can, therefore, only be estimated by indirect approaches, i.e. inverse modelling. Estimation of the parameter and predictive uncertainty of WDS models is an essential part of the inverse modelling process. Recently, Markov Chain Monte Carlo (MCMC) simulations have gained in popularity in uncertainty analyses due to their effective and efficient exploration of posterior parameter probability density functions (pdf). A Bayesian framework is used to infer prior parameter information via a likelihood function to plausible ranges of posterior parameter pdf. Improved parameter and predictive uncertainty are achieved through the incorporation of prior pdf of parameter values and the use of a generalized likelihood function. We used three prior information sampling schemes to infer the pipe roughness coefficients of WDS models. A hypothetical case study and a real-world WDS case study were used to illustrate the strengths and weaknesses of a particular selection of a prior information pdf. The results obtained show that the level of parameter identifiability (i.e. sensitivity) is an important property for prior pdf selection.We are obliged to Jasper A. Vrugt and Cajo ter Braak for providing the code of the DREAM(ZS) algorithm and graphical post-processing software