Novel Adsorption Cycle for High-Efficiency Adsorption Heat Pumps and Chillers: Modeling and Simulation Results

A novel thermodynamic cycle for adsorption heat pumps and chillers is presented. It shows a significant improvement of the internal heat recovery between the adsorption and the desorption half cycle. A stratified thermal storage, which allows for a temperature-based extraction and insertion of storage fluid, is hydraulically coupled with a single adsorber. The benefit is an increased efficiency by reusing the released heat of adsorption for regeneration of the adsorber and by rendering possible low driving temperature differences. For investigating the second law of this cycle, a dynamic model is employed. The transient behavior of the system and the respective losses because of driving temperature differences at the heat exchangers and losses due to mixing within the storage and to the surroundings are depicted in this one-dimensional model. The model is suitable both for analyzing this advanced cycle as well as for comparisons with other cycles

Thermodynamische und numerische Untersuchung eines neuartigen Sorptionszyklus zur Anwendung in Adsorptionswärmepumpen und -kältemaschinen = Thermodynamic and numerical investigation of a novel sorption cycle for application in adsorption heat pumps and chillers

Beim neuartigen Stratisorp-Zyklus kann durch Integration eines thermischen Schichtspeichers in den Zyklus die interne Wärmerückgewinnung verbessert werden. Dies erhöht die Effizienz, z. B. bei Gaswärmepumpen zur Beheizung von Gebäuden. In dieser Arbeit wurden zwei Modelle zur energetischen und entropischen Analyse und eine neue Methode zur konsistenten Berechnung der Wärmekapazität des Adsorbats entwickelt. Damit wurden umfangreiche Systemuntersuchungen durchgeführt

The Influence of the Image Basis on Modeling and Steganalysis Performance

Abstract. We compare two image bases with respect to their capabilities for image modeling and steganalysis. The first basis consists of wavelets, the second is a Laplacian pyramid. Both bases are used to decompose the image into subbands where the local dependency structure is modeled with a linear Bayesian estimator. Similar to existing approaches, the image model is used to predict coefficient values from their neighborhoods, and the final classification step uses statistical descriptors of the residual. Our findings are counter-intuitive on first sight: Although Laplacian pyramids have better image modeling capabilities than wavelets, steganalysis based on wavelets is much more successful. We present a number of experiments that suggest possible explanations for this result.