Particle filtering in compartmental projection models

Simulation models are important tools for real-time forecasting of pandemics. Models help health decision makers examine interventions and secure strong guidance when anticipating outbreak evolution. However, models usually diverge from the real observations. Stochastics involved in pandemic systems, such as changes in human contact patterns play a substantial role in disease transmissions and are not usually captured in traditional dynamic models. In addition, models of emerging diseases face the challenge of limited epidemiological knowledge about the natural history of disease. Even when the information about natural history is available -- for example for endemic seasonal diseases -- transmission models are often simplified and are involved with omissions. Availability of data streams can provide a view of early days of a pandemic, but fail to predict how the pandemic will evolve. Recent developments of computational statistics algorithms such as Sequential Monte Carlo and Markov Chain Monte Carlo, provide the possibility of creating models based on historical data as well as re-grounding models based on ongoing data observations. The objective of this thesis is to combine particle filtering -- a Sequential Monte Carlo algorithm -- with system dynamics models of pandemics. We developed particle filtering models that can recurrently be re-grounded as new observations become available. To this end, we also examined the effectiveness of this arrangement which is subject to specifics of the configuration (e.g., frequency of data sampling). While clinically-diagnosed cases are valuable incoming data stream during an outbreak, new generation of geo-spatially specific data sources, such as search volumes can work as a complementary data resource to clinical data. As another contribution, we used particle filtering in a model which can be re-grounded based on both clinical and search volume data. Our results indicate that the particle filtering in combination with compartmental models provides accurate projection systems for the estimation of model states and also model parameters (particularly compared to traditional calibration methodologies and in the context of emerging communicable diseases). The results also suggest that more frequent sampling from clinical data improves predictive accuracy outstandingly. The results also present that assumptions to make regarding the parameters associated with the particle filtering itself and changes in contact rate were robust across adequacy of empirical data since the beginning of the outbreak and inter-observation interval. The results also support the use of data from Google search API along with clinical data

A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm.

As the unscented Kalman filtering algorithm is sensitive to the battery model and susceptible to the uncertain noise interference, an improved iterate calculation method is proposed to improve the charged state prediction accuracy of the lithium ion battery packs by introducing a novel splice Kalman filtering algorithm with adaptive robust performance. The battery is modeled by composite equivalent modeling and its parameters are identified effectively by investigating the hybrid power pulse test. The sensitivity analysis is carried out for the model parameters to obtain the influence degree on the prediction effect of different factors, providing a basis of the adaptive battery characterization. Subsequently, its implementation process is carried out including model building and adaptive noise correction that are perceived by the iterate charged state calculation. Its experimental results are analyzed and compared with other algorithms through the physical tests. The polarization resistance is obtained as Rp = 16.66 mΩ and capacitance is identified as Cp = 13.71 kF. The ohm internal resistance is calculated as Ro = 68.71 mΩ and the charged state has a prediction error of 1.38% with good robustness effect, providing a foundational basis of the power prediction for the lithium ion battery packs

Theoretical investigations of thermoelectric properties in nanostructured materials

Abwärme entsteht in der heutigen Zeit an vielen Stellen, sei es bei der Umwandlung von primären Energieträgern zu sekundären Energieträgern oder bei der Verwendung von sekundären Energieträgern in technischen Prozessen. Beispiele für solche Prozesse gibt es unter anderem in der Industrie und im Transportwesen. Bei den industriellen Prozessen sind vor allem die energieintensiven Prozesse wie Stahlherstellung oder das Herstellen von Keramiken zu nennen. Im Transportwesen entsteht Abwärme hauptsächlich bei der Verbrennung von fossilen Brennstoffen in den verschiedenen Motoren. Mit Hilfe von thermoelektrischen Effekten ist es möglich, diese anfallende Abwärme in elektrische Energie umzuwandeln und somit nutzbar zu machen. Der Wirkungsgrad von thermoelektrischen Generatoren hängt maßgeblich von den verwendeten Materialien ab. Ein Maß für die Eignung eines Materials zur thermoelektrischen Anwendung ist der Gütefaktor ZT. ZT=(sigma S^2 T)/kappa, wobei sigma die elektrische Leitfähigkeit ist, S der Seebeck Koeffizient ist, T die Temperatur und kappa die thermische Leitfähigkeit ist. Eine Erhöhung des Gütefaktors ZT bedeutet auch eine Erhöhung des Wirkungsgrades. In dieser Arbeit werden zwei übliche Strategien untersucht, die zu einer Erhöhung des Gütefaktors führen können. Eine Strategie ist das Energiefiltern an Korngrenzen. Dabei wird davon ausgegangen, dass eine elektrostatische Barriere an den Korngrenzen den Elektronentransport derart beeinflusst, dass es zu einer Erhöhung des Powerfaktors (sigma S^2) kommt. In dieser Arbeit wird zur Beschreibung der Korngrenze das Modell der doppel-Schottky-Barriere benutzt und der Elektronentransport für einen typischen Halbleiter im Landauer-Modell berechnet. Eine Erhöhung des Powerfaktors konnte hierbei aber nicht beobachtet werden. Eine weitere Möglichkeit den Gütefaktor zu erhöhen führt über die thermische Leitfähigkeit. Für Halbleiter wird die thermische Leitfähigkeit typischerweise vom Gitteranteil dominiert. Kann man den Gitteranteil reduzieren, erhöht sich somit der Gütefaktor. In dieser Arbeit werden an verschiedenen Materialsystemen mehrere Möglichkeiten untersucht, den Gitteranteil zu reduzieren. Es werden der phononische Grenzflächenwiderstand zwischen ZnO und ZnS im sogenannten „diffuse missmatch modell“ berechnet. Aus dem Grenzflächenwiderstand kann dann die spezifische Wärmeleitfähigkeit von ZnO/ZnS-Schichtsystemen in Abhängigkeit der Grenzflächendichte berechnet werden. Es kann gezeigt werden, dass die spezifische Wärmeleitfähigkeit in einem ZnO/ZnS-Schichtsystem im Vergleich zu den reinen Materialen ZnO und ZnS drastisch reduziert ist. Eine weitere Möglichkeit, die spezifische Wärmeleitfähigkeit durch eine Kombination der Materialsysteme ZnO und ZnS zu reduzieren, bietet das Zinkoxisulfid ZnO(x)S(1-x). In dieser Arbeit werden daher Phononenstreuung in ZnO an Schwefelatomen und Phononenstreuung in ZnS an Sauerstoffatomen berechnet. Die Ergebnisse dieser Berechnungen zeigen, dass es zu einer starken Phononenstreuung an den entsprechenden Fremdatomen kommt, wobei die hochenergetischen Phononen stärker gestreut werden als die niederenergetischen Phononen. Zusätzlich zu dem Phononentransport in ZnO/ZnS wird auch der Phononentransport in Si-Isotopen-Multischichtsystemen untersucht. Dabei werden zwei prinzipiell verschiedene Systeme modelliert. Ein System besteht aus periodisch angeordneten Isotopenschichten gleicher Schichtdicke, während das andere System aus Isotopenschichten mit jeweils zufälliger Schichtdicke besteht. Berechnet wird der Wärmewiderstand in Abhängigkeit der Anzahl an Schichten. In der periodischen Anordnung steigt der Wärmewiderstand mit der Anzahl der Schichten bis zu einer Anzahl von 16 an. Erhöht man darüber hinaus die Anzahl der Schichten, bleibt der Wärmewiderstand konstant. Eine solche Sättigung wird bei der zufälligen Anordnung nicht beobachtet. Hier steigt der Wärmewiderstand mit der Anzahl der Schichten immer weiter an. Die Ergebnisse dieser Arbeit zeigen, dass der Phononentransport durch Nanostrukturierung für thermoelektrische Anwendungen günstig beeinflusst werden kann. Ein positiver Effekt durch das Energiefiltern für den Elektronentransport kann allerdings nicht bestätigt werden.Today there are many technological processes in which waste heat is produced. Waste heat often occurs in the transformation from primary energy to final energy or when final energy is consumed. Most of the waste heat occurs in energy-intensive industrial processes like the production of steel or ceramics. A lot of waste heat also occurs in transportation, where the combustion of fossil fuels is rather ineffective. Using thermoelectricity it is possibly to transform a fraction of this waste heat into electric power. The degree of efficiency of thermoelectric generators depends on the materials used. A measure of the suitability of a material is the so called figure of merit ZT. ZT=(sigma S^2 T)/kappa, Where sigma is the electric conductivity, S is the Seebeck coefficient, T is the temperature and kappa is the thermal conductivity. An increase in the figure of merit ZT would result an increase in the efficiency. In this work two common approaches believed to increase the figure of merit are investigated. One approach to increase the figure of merit is the so called energy filtering at grain boundaries. At grain boundaries electrostatic barriers can occur which can alter the electronic transport. It is believed that this could lead to an increase in the powerfactor (sigma S^2). In this work the barriers in the grain boundaries are described by using the model of a double-Schottky-barrier. The impact of these barriers on the electronic transport is calculated. An increase in the powerfactor is not observed. Another approach to increase the figure of merit results in a decrease of the thermal conductivity. In semiconductors the thermal conductivity is dominated by the lattice part. A reduction in the lattice part can therefore decrease the total thermal conductivity substantially. In this work several possibilities to decrease the lattice part of the thermal conductivity are examined for several material systems. The phononic interface conductance between ZnO and Zns is calculated within the diffuse mismatch model. Using the interface conductance it is also possible to calculate the specific thermal conductivity of a ZnO/ZnS layer system. It is shown that the thermal conductivity of a ZnO/ZnS layer system can be reduced compared to the thermal conductivity of the pure materials ZnO and ZnS. Another possibility to reduce the thermal conductivity of the material system ZnO and ZnS can be achieved by mixing them to form zincoxysulfide (ZnO(x)S(1-x) ). In this work phonon scattering in ZnO on sulfur atoms and phonon scattering in ZnS on oxygen atoms are calculated. The results show a strong phonon scattering on the corresponding impurities which is stronger for high energy phonons. Phonon transport is also calculated for Si-isotope-superlattices. Two different arrangements are investigated, a periodic arrangement in which each isotopic layer has the same thickness, and a random arrangement, with random layer thickness. For both arrangements the thermal resistance is calculated in dependency of the number of layers. In the periodic arrangement the thermal resistance increases with increasing number of layers. However, increasing the number of layers beyond 16 does not have an effect on the thermal resistance. For the random arrangement such a saturation is not observed. The results in this work show that nano-structuring is an effective tool to manipulate the phonon transport in a way that beneficial to thermoelectric applications. A positive effect on the electronic transport due to the energy filtering is not observed

Overcoming Challenges in Predictive Modeling of Laser-Plasma Interaction Scenarios. The Sinuous Route from Advanced Machine Learning to Deep Learning

The interaction of ultrashort and intense laser pulses with solid targets and dense plasmas is a rapidly developing area of physics, this being mostly due to the significant advancements in laser technology. There is, thus, a growing interest in diagnosing as accurately as possible the numerous phenomena related to the absorption and reflection of laser radiation. At the same time, envisaged experiments are in high demand of increased accuracy simulation software. As laser-plasma interaction modelings are experiencing a transition from computationally-intensive to data-intensive problems, traditional codes employed so far are starting to show their limitations. It is in this context that predictive modelings of laser-plasma interaction experiments are bound to reshape the definition of simulation software. This chapter focuses an entire class of predictive systems incorporating big data, advanced machine learning algorithms and deep learning, with improved accuracy and speed. Making use of terabytes of already available information (literature as well as simulation and experimental data) these systems enable the discovery and understanding of various physical phenomena occurring during interaction, hence allowing researchers to set up controlled experiments at optimal parameters. A comparative discussion in terms of challenges, advantages, bottlenecks, performances and suitability of laser-plasma interaction predictive systems is ultimately provided