The dispersion of spray drops within a two-phase-flow is signified by an deformable phase interface where material properties suddenly change. The process of atomisation could previously only be modelled for standard applications simultaneously coupled with substantial simulations. In this case the process of atomisation is not coupled to the direct numerical simulation (DNS) of the interaction between two liquids. The approach of disintegration is modelled by statistics, describing the probability of generating liquid droplets. Therefore consideration of potential time and length for an intact liquid core are supposed from standard applications [1, 2]. Using this way of describing atomisation is utilised in a CFD-Code and then compared with PDA-measurements to control the process capability of this implementation.
Zusammenfassung Zweiphasenströmungen zeichnen sich durch eine bewegliche und deformierbare Phasengrenze aus. An dieser Phasengrenze ändern sich die Stoffeigenschaften sprunghaft. Die Erfassung und Beschreibung dieser Grenzflächeneigenschaften ist sehr aufwendig, so dass die numerische Modellierung von Zerstäubungsprozessen bisher nur für exemplarische Einzelfälle eingesetzt wird. Bei dieser Simulation von Zerstäubungsprozessen ist ein hoher Rechenaufwand gegeben. Der Vorgang des Strahlzerfalls lässt sich, wie im Folgenden dargestellt, mittels Zerfallswahrscheinlichkeiten abbilden. Aerodynamische Grenzflächenbetrachtungen fließen bei dieser Betrachtungsweise nicht in das Modell ein. Bei der Verwendung von Zerfallswahrscheinlichkeiten werden Annahmen zu Länge und Zeit der intakten Strahllänge der flüssigen Phase getroffen, die sich aus ähnlichen technischen Fragestellungen in direkten numerischen Simulationen (DNS) ableiten lassen [1,2]. Durch Anwendung von statistisch erzeugten Tropfenspektren in einem Strömungscode und durch Messung mittels Phasen-Doppler-Anemometrie erfolgt die Überprüfung der Aussagefähigkeit des statistischen Modells.
We have developed a self-consistent quantum mechanical Monte Carlo device simulator that takes electron transport in quantized states into consideration. Two-dimensional quantized states in MOSFET channels are constructed from one-dimensional solutions of the Schrödinger equation at different positions along the channel, and the Schrödinger and Poisson equations are solved self-consistently in terms of electron concentration and electrostatic potential distribution. The channel electron concentration, velocity and drain currents are calculated with the one particle Monte Carlo approach incorporating the intra-valley acoustic phonon and inter-valley phonon scattering mechanisms. This simulator was applied to a 70 nm n-MOSFET transistor, and we found that current mostly flows through the lowest subband and transport is quasi-ballistic near the source junction. To quantitatively estimate the performance of advanced devices, we have developed an inversion carrier transport simulator based on a full-band model. Our simulation method enables us to evaluate device characteristics and analyze the transport properties of ultra-small MOSFETs. 相似文献
Exact knowledge of natural gas composition is essential in custody transfer to determine the energy content of the delivery. However, for liquefied natural gas (LNG), a reliable composition determination is difficult. Here, we describe the design of a laboratory-scale reference liquefier that enables the validation and calibration of optical spectroscopy sensors by providing them with a sample of metrologically traceable composition. Hence, it is crucial to avoid fractionation of the sample during liquefaction. This is realized by supercritical liquefaction of a reference gas mixture in conjunction with a special vapor–liquid-equilibrium (VLE) cell. As this is a demanding high-pressure application, low-pressure condensation as liquefaction method was also assessed. Through experimental investigations and VLE calculations, preservation of the composition of the produced liquid sample during condensation was studied. We conclude that under optimized conditions, validation, and calibration measurements of optical sensors can be performed on condensed liquids, which, however, needs further confirmation. 相似文献
Inverse form finding aims in determining the optimal material configuration of a workpiece for a specific forming process. A gradient- and parameter-free (nodal-based) form finding approach has recently been developed, which can be coupled non-invasively as a black box to arbitrary finite element software. Additionally the algorithm is independent from the constitutive behavior. Consequently, the user has not to struggle with the underlying optimization theory behind. Benchmark tests showed already that the approach works robustly and efficiently. This contribution demonstrates that the optimization algorithm is also applicable to more sophisticated forming processes including orthotropic large strain plasticity, combined hardening and frictional contact. A cup deep drawing process with solid-shell elements and a combined deep drawing and upsetting process to form a functional component with external teeth are investigated. 相似文献
The main principle of diagnostic pathology is the reliable interpretation of individual cells in context of the tissue architecture. Especially a confident examination of bone marrow specimen is dependent on a valid classification of myeloid cells. In this work, we propose a novel rotation-invariant learning scheme for multi-class echo state networks (ESNs), which achieves very high performance in automated bone marrow cell classification. Based on representing static images as temporal sequence of rotations, we show how ESNs robustly recognize cells of arbitrary rotations by taking advantage of their short-term memory capacity. The performance of our approach is compared to a classification random forest that learns rotation-invariance in a conventional way by exhaustively training on multiple rotations of individual samples. The methods were evaluated on a human bone marrow image database consisting of granulopoietic and erythropoietic cells in different maturation stages. Our ESN approach to cell classification does not rely on segmentation of cells or manual feature extraction and can therefore directly be applied to image data.