Study of escaping electron dynamics and applications from high-power laser-plasma interactions

In recent years, high intensity laser-matter interactions (> 1018 W/cm2) have been shown to produce bright, compact sources of many different particles. These include x-rays, neutrons, protons and electrons, which can be used in applications such as x-ray and electron radiography. The potential use of these sources for industrial applications is promising. However, the scalability and tuning of the sources needs to be understood at a fundamental level. This thesis reports on three aspects of the development and application of these sources; the first two discuss applications of laser-plasma interactions. Firstly, the generation, characterisation and tunability of high-energy x-rays (= 200 keV) produced by the hot-electrons generated inside a solid target for the application of x-ray radiography. The characterisation of the x-ray source is conducted using a novel scintillator based absorption spectrometer. This source of x-rays was then used to radiograph a high density test object. Secondly, a novel technique of x-ray backscatter is investigated numerically and demonstrated experimentally for the first time on a laser facility. This uses the high energy electrons generated via wakefield acceleration to probe deeper into materials than traditional backscatter techniques. Finally, an investigation is reported examining the fundamental dynamics of electrons escaping from solid targets under different irradiation conditions. Experimentally, the number of escaping electrons was shown to maximise for certain laser illumination conditions; this was also explored using PIC simulations. The new results discussed in these three sections produce important new understanding of laser-driven x-ray generation and its application to penetrative probing and imaging for possible future industrial applications as well as the understanding of escaping electron dynamics.In recent years, high intensity laser-matter interactions (> 1018 W/cm2) have been shown to produce bright, compact sources of many different particles. These include x-rays, neutrons, protons and electrons, which can be used in applications such as x-ray and electron radiography. The potential use of these sources for industrial applications is promising. However, the scalability and tuning of the sources needs to be understood at a fundamental level. This thesis reports on three aspects of the development and application of these sources; the first two discuss applications of laser-plasma interactions. Firstly, the generation, characterisation and tunability of high-energy x-rays (= 200 keV) produced by the hot-electrons generated inside a solid target for the application of x-ray radiography. The characterisation of the x-ray source is conducted using a novel scintillator based absorption spectrometer. This source of x-rays was then used to radiograph a high density test object. Secondly, a novel technique of x-ray backscatter is investigated numerically and demonstrated experimentally for the first time on a laser facility. This uses the high energy electrons generated via wakefield acceleration to probe deeper into materials than traditional backscatter techniques. Finally, an investigation is reported examining the fundamental dynamics of electrons escaping from solid targets under different irradiation conditions. Experimentally, the number of escaping electrons was shown to maximise for certain laser illumination conditions; this was also explored using PIC simulations. The new results discussed in these three sections produce important new understanding of laser-driven x-ray generation and its application to penetrative probing and imaging for possible future industrial applications as well as the understanding of escaping electron dynamics

Modelling temperature-dependent larval development and\ud subsequent demographic Allee effects in adult populations of the alpine butterfly Parnassius smintheus

Climate change has been attributed as a driver of changes to ecological systems worldwide and understanding the effects of climate change at individual, population, community, and ecosystem levels has become a primary concern of ecology. One avenue toward understanding the impacts of climate change on an ecosystem is through the study of environmentally sensitive species. Butterflies are sensitive to climatic changes due to their reliance on environmental cues such as temperature and photoperiod, which regulate the completion of life history stages. As such, the population dynamics of butterflies may offer insight into the impacts of climate change on the health of an ecosystem. In this paper we study the effects of rearing temperature on the alpine butterfly Parnassius smintheus (Rocky Mountain Apollo), both directly through individual phenological changes and indirectly through adult reproductive success at the population level. Our approach is to formulate a mathematical model of individual development parameterized by experimental data and link larval development to adult reproductive success. A Bernoulli process model describes temperature-dependent larval phenology, and a system of ordinary differential equations is used to study impacts on reproductive success. The phenological model takes field temperature data as its input and predicts a temporal distribution of adult emergence, which in turn controls the dynamics of the reproductive success model. We find that warmer spring and summer temperatures increase reproductive success, while cooler temperatures exacerbate a demographic Allee effect, suggesting that observed yearly fluctuations in P. smintheus population size may be driven by inter-annual temperature variability. Model predictions are validated against mark-recapture field data from 2001 and 2003 − 2009

Development of overturning circulation in sloping waterbodies due to surface cooling

This work was supported by the Swiss National Science Foundation (project Buoyancy driven nearshore transport in lakes, HYPOlimnetic THErmal SIphonS, HYPOTHESIS, reference 175919) and by the Physics of Aquatic Systems Laboratory (APHYS), EPFL.Cooling the surface of freshwater bodies, whose temperatures are above the temperature of maximum density, can generate differential cooling between shallow and deep regions. When surface cooling occurs over a long enough period, the thermally induced cross-shore pressure gradient may drive an overturning circulation, a phenomenon called ‘thermal siphon’. However, the conditions under which this process begins are not yet fully characterised. Here, we examine the development of thermal siphons driven by a uniform loss of heat at the air–water interface in sloping, stratified basins. For a two-dimensional framework, we derive theoretical time and velocity scales associated with the transition from Rayleigh–Bénard type convection to a horizontal overturning circulation across the shallower sloping basin. This transition is characterised by a three-way horizontal momentum balance, in which the cross-shore pressure gradient balances the inertial terms before reaching a quasi-steady regime. We performed numerical and field experiments to test and show the robustness of the analytical scaling, describe the convective regimes and quantify the cross-shore transport induced by thermal siphons. Our results are relevant for understanding the nearshore fluid dynamics induced by nighttime or seasonal surface cooling in lakes and reservoirs.Swiss National Science Foundation (SNSF) European Commission 175919Physics of Aquatic Systems Laboratory (APHYS), EPF

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202